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Dalli J, Epperlein JP, Hardy NP, Khan MF, Mac Aonghusa P, Cahill RA. Clinical and computational development of a patient-calibrated ICGFA bowel transection recommender. Surg Endosc 2024; 38:3212-3222. [PMID: 38637339 PMCID: PMC11133155 DOI: 10.1007/s00464-024-10827-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/23/2024] [Indexed: 04/20/2024]
Abstract
INTRODUCTION Intraoperative indocyanine green fluorescence angiography (ICGFA) aims to reduce colorectal anastomotic complications. However, signal interpretation is inconsistent and confounded by patient physiology and system behaviours. Here, we demonstrate a proof of concept of a novel clinical and computational method for patient calibrated quantitative ICGFA (QICGFA) bowel transection recommendation. METHODS Patients undergoing elective colorectal resection had colonic ICGFA both immediately after operative commencement prior to any dissection and again, as usual, just before anastomotic construction. Video recordings of both ICGFA acquisitions were blindly quantified post hoc across selected colonic regions of interest (ROIs) using tracking-quantification software and computationally compared with satisfactory perfusion assumed in second time-point ROIs, demonstrating 85% agreement with baseline ICGFA. ROI quantification outputs detailing projected perfusion sufficiency-insufficiency zones were compared to the actual surgeon-selected transection/anastomotic construction site for left/right-sided resections, respectively. Anastomotic outcomes were recorded, and tissue lactate was also measured in the devascularised colonic segment in a subgroup of patients. The novel perfusion zone projections were developed as full-screen recommendations via overlay heatmaps. RESULTS No patient suffered intra- or early postoperative anastomotic complications. Following computational development (n = 14) the software recommended zone (ROI) contained the expert surgical site of transection in almost all cases (Jaccard similarity index 0.91) of the nine patient validation series. Previously published ICGFA time-series milestone descriptors correlated moderately well, but lactate measurements did not. High resolution augmented reality heatmaps presenting recommendations from all pixels of the bowel ICGFA were generated for all cases. CONCLUSIONS By benchmarking to the patient's own baseline perfusion, this novel QICGFA method could allow the deployment of algorithmic personalised NIR bowel transection point recommendation in a way fitting existing clinical workflow.
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Affiliation(s)
- Jeffrey Dalli
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, Catherine McAuley Centre, 21 Nelson St, Dublin 7, D07 KX5K, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Niall P Hardy
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, Catherine McAuley Centre, 21 Nelson St, Dublin 7, D07 KX5K, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Mohammad Faraz Khan
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, Catherine McAuley Centre, 21 Nelson St, Dublin 7, D07 KX5K, Ireland
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland
| | | | - Ronan A Cahill
- UCD Centre for Precision Surgery, School of Medicine, University College Dublin, Catherine McAuley Centre, 21 Nelson St, Dublin 7, D07 KX5K, Ireland.
- Department of Surgery, Mater Misericordiae University Hospital, Dublin, Ireland.
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Denecke K, May R, Rivera Romero O. Potential of Large Language Models in Health Care: Delphi Study. J Med Internet Res 2024; 26:e52399. [PMID: 38739445 PMCID: PMC11130776 DOI: 10.2196/52399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 10/10/2023] [Accepted: 04/19/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence. LLMs have been shown to be highly effective for a range of tasks in natural language processing (NLP), including classification and information extraction tasks and generative applications. OBJECTIVE The aim of this adapted Delphi study was to collect researchers' opinions on how LLMs might influence health care and on the strengths, weaknesses, opportunities, and threats of LLM use in health care. METHODS We invited researchers in the fields of health informatics, nursing informatics, and medical NLP to share their opinions on LLM use in health care. We started the first round with open questions based on our strengths, weaknesses, opportunities, and threats framework. In the second and third round, the participants scored these items. RESULTS The first, second, and third rounds had 28, 23, and 21 participants, respectively. Almost all participants (26/28, 93% in round 1 and 20/21, 95% in round 3) were affiliated with academic institutions. Agreement was reached on 103 items related to use cases, benefits, risks, reliability, adoption aspects, and the future of LLMs in health care. Participants offered several use cases, including supporting clinical tasks, documentation tasks, and medical research and education, and agreed that LLM-based systems will act as health assistants for patient education. The agreed-upon benefits included increased efficiency in data handling and extraction, improved automation of processes, improved quality of health care services and overall health outcomes, provision of personalized care, accelerated diagnosis and treatment processes, and improved interaction between patients and health care professionals. In total, 5 risks to health care in general were identified: cybersecurity breaches, the potential for patient misinformation, ethical concerns, the likelihood of biased decision-making, and the risk associated with inaccurate communication. Overconfidence in LLM-based systems was recognized as a risk to the medical profession. The 6 agreed-upon privacy risks included the use of unregulated cloud services that compromise data security, exposure of sensitive patient data, breaches of confidentiality, fraudulent use of information, vulnerabilities in data storage and communication, and inappropriate access or use of patient data. CONCLUSIONS Future research related to LLMs should not only focus on testing their possibilities for NLP-related tasks but also consider the workflows the models could contribute to and the requirements regarding quality, integration, and regulations needed for successful implementation in practice.
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Affiliation(s)
| | - Richard May
- Harz University of Applied Sciences, Wernigerode, Germany
| | - Octavio Rivera Romero
- Instituto de Ingeniería Informática (I3US), Universidad de Sevilla, Sevilla, Spain
- Department of Electronic Technology, Universidad de Sevilla, Sevilla, Spain
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Tan YY, Kang HG, Lee CJ, Kim SS, Park S, Thakur S, Da Soh Z, Cho Y, Peng Q, Tham YC, Rim TH, Cheng CY. Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging. EYE AND VISION (LONDON, ENGLAND) 2024; 11:17. [PMID: 38711111 PMCID: PMC11071258 DOI: 10.1186/s40662-024-00384-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/17/2024] [Indexed: 05/08/2024]
Abstract
BACKGROUND Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care. MAIN TEXT This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care. CONCLUSION AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.
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Affiliation(s)
| | - Hyun Goo Kang
- Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chan Joo Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung Soo Kim
- Division of Retina, Severance Eye Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sungha Park
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yunnie Cho
- Mediwhale Inc, Seoul, Republic of Korea
- Department of Education and Human Resource Development, Seoul National University Hospital, Seoul, South Korea
| | - Qingsheng Peng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Mediwhale Inc, Seoul, Republic of Korea
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Mediwhale Inc, Seoul, Republic of Korea.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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Ross J, Hammouche S, Chen Y, Rockall AG. Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment. Clin Radiol 2024; 79:338-345. [PMID: 38360516 DOI: 10.1016/j.crad.2024.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 01/24/2024] [Accepted: 01/29/2024] [Indexed: 02/17/2024]
Abstract
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.
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Affiliation(s)
- J Ross
- Department of Cancer and Surgery, Imperial College London, UK.
| | - S Hammouche
- Department of Cancer and Surgery, Imperial College London, UK
| | - Y Chen
- School of Medicine, University of Nottingham, UK
| | - A G Rockall
- Department of Cancer and Surgery, Imperial College London, UK
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Varghese C, Harrison EM, O'Grady G, Topol EJ. Artificial intelligence in surgery. Nat Med 2024; 30:1257-1268. [PMID: 38740998 DOI: 10.1038/s41591-024-02970-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery remain relatively nascent. Here we review the integration of AI in the field of surgery, centering our discussion on multifaceted improvements in surgical care in the preoperative, intraoperative and postoperative space. The emergence of foundation model architectures, wearable technologies and improving surgical data infrastructures is enabling rapid advances in AI interventions and utility. We discuss how maturing AI methods hold the potential to improve patient outcomes, facilitate surgical education and optimize surgical care. We review the current applications of deep learning approaches and outline a vision for future advances through multimodal foundation models.
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Affiliation(s)
- Chris Varghese
- Department of Surgery, University of Auckland, Auckland, New Zealand
| | - Ewen M Harrison
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Greg O'Grady
- Department of Surgery, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Eric J Topol
- Scripps Research Translational Institute, La Jolla, CA, USA.
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Cohen JF, Bossuyt PMM. TRIPOD+AI: an updated reporting guideline for clinical prediction models. BMJ 2024; 385:q824. [PMID: 38626949 DOI: 10.1136/bmj.q824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Affiliation(s)
- Jérémie F Cohen
- Centre of Research in Epidemiology and Statistics (CRESS), INSERM, EPOPé Research Team, Université Paris Cité, 75014 Paris, France
- Department of General Pediatrics and Pediatric Infectious Diseases, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris, Université Paris Cité, Paris, France
| | - Patrick M M Bossuyt
- Department of Epidemiology and Data Science, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, Netherlands
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Pinsky MR, Bedoya A, Bihorac A, Celi L, Churpek M, Economou-Zavlanos NJ, Elbers P, Saria S, Liu V, Lyons PG, Shickel B, Toral P, Tscholl D, Clermont G. Use of artificial intelligence in critical care: opportunities and obstacles. Crit Care 2024; 28:113. [PMID: 38589940 PMCID: PMC11000355 DOI: 10.1186/s13054-024-04860-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed. MAIN BODY Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools. CONCLUSIONS AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.
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Affiliation(s)
- Michael R Pinsky
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA.
| | - Armando Bedoya
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
- Division of Pulmonary Critical Care Medicine, Duke University School of Medicine, Durham, NC, 27713, USA
| | - Azra Bihorac
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
| | - Leo Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Matthew Churpek
- Department of Medicine, University of Wisconsin, 600 Highland Ave, Madison, WI, 53792, USA
| | - Nicoleta J Economou-Zavlanos
- Algorithm-Based Clinical Decision Support (ABCDS) Oversight, Office of Vice Dean of Data Science, School of Medicine, Duke University, Durham, NC, 27705, USA
| | - Paul Elbers
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Suchi Saria
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins Medical Institutions, Johns Hopkins University, 333 Malone Hall, 300 Wolfe Street, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins School of Medicine, AI and Health Lab, Johns Hopkins University, Baltimore, MD, USA
- Bayesian Health, New york, NY, 10282, USA
| | - Vincent Liu
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
- , 2000 Broadway, Oakland, CA, 94612, USA
| | - Patrick G Lyons
- Department of Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Road, Mail Code UHN67, Portland, OR, 97239-3098, USA
| | - Benjamin Shickel
- Department of Medicine, University of Florida College of Medicine Gainesville, Malachowsky Hall, 1889 Museum Road, Suite 2410, Gainesville, FL, 32611, USA
- Amsterdam UMC, ZH.7D.167, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - Patrick Toral
- Department of Intensive Care, Amsterdam UMC, Amsterdam, NL, USA
- Amsterdam UMC, ZH.7D.165, De Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands
| | - David Tscholl
- Institute of Anesthesiology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland
| | - Gilles Clermont
- Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA
- VA Pittsburgh Health System, 131A Building 30, 4100 Allequippa St, Pittsburgh, PA, 15240, USA
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Zhang Y, Liu B, Bunting KV, Brind D, Thorley A, Karwath A, Lu W, Zhou D, Wang X, Mobley AR, Tica O, Gkoutos GV, Kotecha D, Duan J. Development of automated neural network prediction for echocardiographic left ventricular ejection fraction. Front Med (Lausanne) 2024; 11:1354070. [PMID: 38686369 PMCID: PMC11057494 DOI: 10.3389/fmed.2024.1354070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 03/18/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). Methods This paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey's method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline's accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF. Results This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment. Conclusion The automated neural network-based calculation of LVEF is comparable to expert clinicians performing time-consuming, frame-by-frame manual evaluations of cardiac systolic function.
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Affiliation(s)
- Yuting Zhang
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Boyang Liu
- Manchester University NHS Foundation Trust, Manchester, United Kingdom
| | - Karina V. Bunting
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre and West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - David Brind
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Centre for Health Data Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Alexander Thorley
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Andreas Karwath
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- Centre for Health Data Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Wenqi Lu
- Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom
| | - Diwei Zhou
- Department of Mathematical Sciences, Loughborough University, Loughborough, United Kingdom
| | - Xiaoxia Wang
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre and West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Alastair R. Mobley
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre and West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Otilia Tica
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Centre for Health Data Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
| | - Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre and West Midlands NHS Secure Data Environment, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Health Data Research UK Midlands, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Jinming Duan
- School of Computer Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
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Flanagin A, Pirracchio R, Khera R, Berkwits M, Hswen Y, Bibbins-Domingo K. Reporting Use of AI in Research and Scholarly Publication-JAMA Network Guidance. JAMA 2024; 331:1096-1098. [PMID: 38451540 DOI: 10.1001/jama.2024.3471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Affiliation(s)
| | | | - Rohan Khera
- Associate Editor, JAMA
- Yale School of Medicine, New Haven, Connecticut
| | | | - Yulin Hswen
- Associate Editor, JAMA
- University of California, San Francisco
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Wong DC, Williams S. Artificial intelligence analysis of videos to augment clinical assessment: an overview. Neural Regen Res 2024; 19:717-718. [PMID: 37843200 PMCID: PMC10664118 DOI: 10.4103/1673-5374.382249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/30/2023] [Accepted: 06/27/2023] [Indexed: 10/17/2023] Open
Affiliation(s)
- David C. Wong
- University of Leeds; Stefan Williams, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Stefan Williams
- University of Leeds; Stefan Williams, Leeds Teaching Hospitals Trust, Leeds, UK
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van der Vegt A, Campbell V, Zuccon G. Why clinical artificial intelligence is (almost) non-existent in Australian hospitals and how to fix it. Med J Aust 2024; 220:172-175. [PMID: 38146620 DOI: 10.5694/mja2.52195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/27/2023] [Indexed: 12/27/2023]
Affiliation(s)
- Anton van der Vegt
- Centre for Health Services Research, University of Queensland, Brisbane, QLD
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Gianola S, Bargeri S, Castellini G, Cook C, Palese A, Pillastrini P, Salvalaggio S, Turolla A, Rossettini G. Performance of ChatGPT Compared to Clinical Practice Guidelines in Making Informed Decisions for Lumbosacral Radicular Pain: A Cross-sectional Study. J Orthop Sports Phys Ther 2024; 54:1-7. [PMID: 38284363 DOI: 10.2519/jospt.2024.12151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/30/2024]
Abstract
OBJECTIVE: To compare the accuracy of an artificial intelligence chatbot to clinical practice guidelines (CPGs) recommendations for providing answers to complex clinical questions on lumbosacral radicular pain. DESIGN: Cross-sectional study. METHODS: We extracted recommendations from recent CPGs for diagnosing and treating lumbosacral radicular pain. Relative clinical questions were developed and queried to OpenAI's ChatGPT (GPT-3.5). We compared ChatGPT answers to CPGs recommendations by assessing the (1) internal consistency of ChatGPT answers by measuring the percentage of text wording similarity when a clinical question was posed 3 times, (2) reliability between 2 independent reviewers in grading ChatGPT answers, and (3) accuracy of ChatGPT answers compared to CPGs recommendations. Reliability was estimated using Fleiss' kappa (κ) coefficients, and accuracy by interobserver agreement as the frequency of the agreements among all judgments. RESULTS: We tested 9 clinical questions. The internal consistency of text ChatGPT answers was unacceptable across all 3 trials in all clinical questions (mean percentage of 49%, standard deviation of 15). Intrareliability (reviewer 1: κ = 0.90, standard error [SE] = 0.09; reviewer 2: κ = 0.90, SE = 0.10) and interreliability (κ = 0.85, SE = 0.15) between the 2 reviewers was "almost perfect." Accuracy between ChatGPT answers and CPGs recommendations was slight, demonstrating agreement in 33% of recommendations. CONCLUSION: ChatGPT performed poorly in internal consistency and accuracy of the indications generated compared to clinical practice guideline recommendations for lumbosacral radicular pain. J Orthop Sports Phys Ther 2024;54(3):1-7. Epub 29 January 2024. doi:10.2519/jospt.2024.12151.
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Verma AA, Trbovich P, Mamdani M, Shojania KG. Grand rounds in methodology: key considerations for implementing machine learning solutions in quality improvement initiatives. BMJ Qual Saf 2024; 33:121-131. [PMID: 38050138 DOI: 10.1136/bmjqs-2022-015713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 11/04/2023] [Indexed: 12/06/2023]
Abstract
Machine learning (ML) solutions are increasingly entering healthcare. They are complex, sociotechnical systems that include data inputs, ML models, technical infrastructure and human interactions. They have promise for improving care across a wide range of clinical applications but if poorly implemented, they may disrupt clinical workflows, exacerbate inequities in care and harm patients. Many aspects of ML solutions are similar to other digital technologies, which have well-established approaches to implementation. However, ML applications present distinct implementation challenges, given that their predictions are often complex and difficult to understand, they can be influenced by biases in the data sets used to develop them, and their impacts on human behaviour are poorly understood. This manuscript summarises the current state of knowledge about implementing ML solutions in clinical care and offers practical guidance for implementation. We propose three overarching questions for potential users to consider when deploying ML solutions in clinical care: (1) Is a clinical or operational problem likely to be addressed by an ML solution? (2) How can an ML solution be evaluated to determine its readiness for deployment? (3) How can an ML solution be deployed and maintained optimally? The Quality Improvement community has an essential role to play in ensuring that ML solutions are translated into clinical practice safely, effectively, and ethically.
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Affiliation(s)
- Amol A Verma
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Patricia Trbovich
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Centre for Quality Improvement and Patient Safety, Department of Medicine, University of Toronto, Toronto, ON, Canada
- North York General Hospital, Toronto, ON, Canada
| | - Muhammad Mamdani
- Unity Health Toronto, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
| | - Kaveh G Shojania
- Medicine, University of Toronto Faculty of Medicine, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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14
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van der Vegt AH, Campbell V, Mitchell I, Malycha J, Simpson J, Flenady T, Flabouris A, Lane PJ, Mehta N, Kalke VR, Decoyna JA, Es’haghi N, Liu CH, Scott IA. Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain. J Am Med Inform Assoc 2024; 31:509-524. [PMID: 37964688 PMCID: PMC10797271 DOI: 10.1093/jamia/ocad220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/16/2023] Open
Abstract
OBJECTIVE To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework. MATERIALS AND METHODS A systematic review of studies of implemented or trialed real-time clinical deterioration prediction MLAs was undertaken, which identified: how MLA implementation was measured; impact of MLAs on clinical processes and patient outcomes; and barriers, enablers and uncertainties within the implementation process. Review findings were then mapped to the SALIENT end-to-end implementation framework to identify the implementation stages at which these factors applied. RESULTS Thirty-seven articles relating to 14 groups of MLAs were identified, each trialing or implementing a bespoke algorithm. One hundred and seven distinct implementation evaluation metrics were identified. Four groups reported decreased hospital mortality, 1 significantly. We identified 24 barriers, 40 enablers, and 14 uncertainties and mapped these to the 5 stages of the SALIENT implementation framework. DISCUSSION Algorithm performance across implementation stages decreased between in silico and trial stages. Silent plus pilot trial inclusion was associated with decreased mortality, as was the use of logistic regression algorithms that used less than 39 variables. Mitigation of alert fatigue via alert suppression and threshold configuration was commonly employed across groups. CONCLUSIONS : There is evidence that real-world implementation of clinical deterioration prediction MLAs may improve clinical outcomes. Various factors identified as influencing success or failure of implementation can be mapped to different stages of implementation, thereby providing useful and practical guidance for implementers.
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Affiliation(s)
- Anton H van der Vegt
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Victoria Campbell
- Intensive Care Unit, Sunshine Coast Hospital and Health Service, Birtynia, QLD 4575, Australia
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Imogen Mitchell
- Office of Research and Education, Canberra Health Services, Canberra, ACT 2601, Australia
| | - James Malycha
- Department of Critical Care Medicine, The Queen Elizabeth Hospital, Woodville, SA 5011, Australia
| | - Joanna Simpson
- Eastern Health Intensive Care Services, Eastern Health, Box Hill, VIC 3128, Australia
| | - Tracy Flenady
- School of Nursing, Midwifery & Social Sciences, Central Queensland University, Rockhampton, QLD 4701, Australia
| | - Arthas Flabouris
- Intensive Care Department, Royal Adelaide Hospital, Adelaide, SA 5000, Australia
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Chermside, QLD 4032, Australia
| | - Naitik Mehta
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Brisbane, QLD 4001, Australia
| | - Jovie A Decoyna
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Nicholas Es’haghi
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Chun-Huei Liu
- School of Medicine and Dentistry, Griffith University, Gold Coast, QLD 4222, Australia
| | - Ian A Scott
- Centre for Health Services Research, The University of Queensland, Brisbane, QLD 4102, Australia
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
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Hatamnejad A, Higham A, Somani S, Tam ES, Lim E, Khavandi S, de Pennington N, Chiu HH. Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol. BMJ Open Ophthalmol 2024; 9:e001475. [PMID: 38199790 PMCID: PMC10806655 DOI: 10.1136/bmjophth-2023-001475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
INTRODUCTION Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increase effectiveness and decrease burdens placed on patients and the healthcare system. This study assesses the use of 'Dora', an example of an AI assistant that is able to deliver a regulated autonomous, voice-based, natural-language consultation with patients over the telephone. Dora is used in routine practice in the UK, but this study seeks to assess the safety, usability, acceptability and cost-effectiveness of using the technology in Canada. METHODS AND ANALYSIS This is a two-phase prospective single-centred trial. An expected 250 patients will be recruited for each phase of the study. For Phase I of the study, Dora will phone patients at postoperative week 1 and for Phase II of the study, Dora will phone patients within 24hours of their cataract surgery and again at postoperative week 1. We will evaluate the agreement between Dora and a supervising clinician regarding the need for further review based on the patients' symptoms. A random sample of patients will undergo the System Usability Scale followed by an extended semi-structured interview. The primary outcome of agreement between Dora and the supervisor will be assessed using the kappa statistic. Qualitative data from the interviews will further gauge patient opinions about Dora's usability, appropriateness and level of satisfaction. ETHICS AND DISSEMINATION Research Ethics Board William Osler Health System (ID: 22-0044) has approved this study and will be conducted by guidelines of Declaration of Helsinki. Master-linking sheet will contain the patient chart identification (ID), full name, date of birth and study ID. Results will be shared through peer-reviewed journals and presentations at conferences.
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Affiliation(s)
- Amin Hatamnejad
- McMaster University Michael G DeGroote School of Medicine, Hamilton, Ontario, Canada
| | - Aisling Higham
- Ufonia Limited, Oxford, UK
- Royal Berkshire Hospital NHS Foundation Trust, Reading, UK
| | - Sohel Somani
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
| | - Eric S Tam
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
| | - Ernest Lim
- Ufonia Limited, Oxford, UK
- Department of Computer Science, University of York, York, UK
| | | | | | - Hannah H Chiu
- McMaster University Michael G DeGroote School of Medicine, Hamilton, Ontario, Canada
- Department of Opthalmology and Vision Sciences, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- William Osler Health System, Brampton, Ontario, Canada
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16
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Hanada S, Hayashi Y, Subramani S, Thenuwara K. Pioneering the Integration of Artificial Intelligence in Medical Oral Board Examinations. Cureus 2024; 16:e52318. [PMID: 38357084 PMCID: PMC10866608 DOI: 10.7759/cureus.52318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
We evaluated the use of ChatGPT-4, an advanced artificial intelligence (AI) language model, in medical oral examinations, specifically in anesthesiology. Initially proven adept in written examinations, ChatGPT-4's performance was tested against oral board sample sessions of the American Board of Anesthesiology. Modifications were made to ensure responses were concise and conversationally natural, simulating real patient consultations or oral examinations. The results demonstrate ChatGPT-4's impressive adaptability and potential in oral board examinations as a training and assessment tool in medical education, indicating new avenues for AI application in this field.
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Affiliation(s)
- Satoshi Hanada
- Anesthesia, University of Iowa Hospitals and Clinics, Iowa City, USA
| | - Yuri Hayashi
- Anesthesia, University of Iowa Hospitals and Clinics, Iowa City, USA
- Department of Anesthesiology and Intensive Care Medicine, Osaka University Graduate School of Medicine, Suita, JPN
| | | | - Kokila Thenuwara
- Anesthesia, University of Iowa Hospitals and Clinics, Iowa City, USA
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Marcus HJ, Ramirez PT, Khan DZ, Layard Horsfall H, Hanrahan JG, Williams SC, Beard DJ, Bhat R, Catchpole K, Cook A, Hutchison K, Martin J, Melvin T, Stoyanov D, Rovers M, Raison N, Dasgupta P, Noonan D, Stocken D, Sturt G, Vanhoestenberghe A, Vasey B, McCulloch P. The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring. Nat Med 2024; 30:61-75. [PMID: 38242979 DOI: 10.1038/s41591-023-02732-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 11/20/2023] [Indexed: 01/21/2024]
Abstract
The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot's development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots-providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.
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Affiliation(s)
- Hani J Marcus
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK.
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK.
| | - Pedro T Ramirez
- Department of Obstetrics and Gynaecology, Houston Methodist Hospital Neal Cancer Center, Houston, TX, USA
| | - Danyal Z Khan
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Hugo Layard Horsfall
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - John G Hanrahan
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Simon C Williams
- Department of Neurosurgery, National Hospital of Neurology and Neurosurgery, London, UK
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - David J Beard
- RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR), Nuffield Dept Orthopaedics, Rheumatology and Musculo-skeletal Sciences, University of Oxford, Oxford, UK
| | - Rani Bhat
- Department of Gynaecological Oncology, Apollo Hospital, Bengaluru, India
| | - Ken Catchpole
- Department of Anaesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew Cook
- NIHR Coordinating Centre and Clinical Trials Unit, University of Southampton, Southampton, UK
| | | | - Janet Martin
- Department of Anesthesia & Perioperative Medicine, University of Western Ontario, Ontario, Canada
| | - Tom Melvin
- Department of Medical Gerontology, School of Medicine, Trinity College Dublin, Dublin, Republic of Ireland
| | - Danail Stoyanov
- Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS), London, UK
| | - Maroeska Rovers
- Department of Medical Imaging, Radboudumc, Nijmegen, the Netherlands
| | - Nicholas Raison
- Department of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Prokar Dasgupta
- King's Health Partners Academic Surgery, King's College London, London, UK
| | | | - Deborah Stocken
- RCSEng Surgical Trials Centre, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK
| | | | - Anne Vanhoestenberghe
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Baptiste Vasey
- Department of Surgery, Geneva University Hospital, Geneva, Switzerland
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Peter McCulloch
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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18
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Zhong J, Xing Y, Lu J, Zhang G, Mao S, Chen H, Yin Q, Cen Q, Jiang R, Hu Y, Ding D, Ge X, Zhang H, Yao W. The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study. BMC Med Res Methodol 2023; 23:292. [PMID: 38093215 PMCID: PMC10717715 DOI: 10.1186/s12874-023-02117-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Complete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables. METHODS This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: "active strong", "active weak", "passive moderate", "passive weak", and "none". The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis. RESULTS We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals. CONCLUSIONS The general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.
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Affiliation(s)
- Jingyu Zhong
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Yue Xing
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Junjie Lu
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Guangcheng Zhang
- Department of Orthopedics, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Shiqi Mao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Haoda Chen
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Qian Yin
- Department of Pathology, Shanghai Sixth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Qingqing Cen
- Department of Dermatology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
| | - Run Jiang
- Department of Pharmacovigilance, Shanghai Hansoh BioMedical Co., Ltd., Shanghai, 201203, China
| | - Yangfan Hu
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Defang Ding
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Xiang Ge
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Weiwu Yao
- Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200336, China.
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Lanotte F, O’Brien MK, Jayaraman A. AI in Rehabilitation Medicine: Opportunities and Challenges. Ann Rehabil Med 2023; 47:444-458. [PMID: 38093518 PMCID: PMC10767220 DOI: 10.5535/arm.23131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 11/23/2023] [Indexed: 01/03/2024] Open
Abstract
Artificial intelligence (AI) tools are increasingly able to learn from larger and more complex data, thus allowing clinicians and scientists to gain new insights from the information they collect about their patients every day. In rehabilitation medicine, AI can be used to find patterns in huge amounts of healthcare data. These patterns can then be leveraged at the individual level, to design personalized care strategies and interventions to optimize each patient's outcomes. However, building effective AI tools requires many careful considerations about how we collect and handle data, how we train the models, and how we interpret results. In this perspective, we discuss some of the current opportunities and challenges for AI in rehabilitation. We first review recent trends in AI for the screening, diagnosis, treatment, and continuous monitoring of disease or injury, with a special focus on the different types of healthcare data used for these applications. We then examine potential barriers to designing and integrating AI into the clinical workflow, and we propose an end-to-end framework to address these barriers and guide the development of effective AI for rehabilitation. Finally, we present ideas for future work to pave the way for AI implementation in real-world rehabilitation practices.
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Affiliation(s)
- Francesco Lanotte
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Megan K. O’Brien
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
| | - Arun Jayaraman
- Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan AbilityLab, Chicago, IL, United States
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States
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20
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Kim DY, Oh HW, Suh CH. Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia. Korean J Radiol 2023; 24:1179-1189. [PMID: 38016678 DOI: 10.3348/kjr.2023.1027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 10/25/2023] [Accepted: 10/26/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVE We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. MATERIALS AND METHODS A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. RESULTS Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. CONCLUSION The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.
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Affiliation(s)
- Dong Yeong Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | | | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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Gauss T, Perkins Z, Tjardes T. Current knowledge and availability of machine learning across the spectrum of trauma science. Curr Opin Crit Care 2023; 29:713-721. [PMID: 37861197 DOI: 10.1097/mcc.0000000000001104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
PURPOSE OF REVIEW Recent technological advances have accelerated the use of Machine Learning in trauma science. This review provides an overview on the available evidence for research and patient care. The review aims to familiarize clinicians with this rapidly evolving field, offer perspectives, and identify existing and future challenges. RECENT FINDINGS The available evidence predominantly focuses on retrospective algorithm construction to predict outcomes. Few studies have explored actionable outcomes, workflow integration, or the impact on patient care. Machine Learning and data science have the potential to simplify data capture and enhance counterfactual causal inference research from observational data to address complex issues. However, regulatory, legal, and ethical challenges associated with the use of Machine Learning in trauma care deserve particular attention. SUMMARY Machine Learning holds promise for actionable decision support in trauma science, but rigorous proof-of-concept studies are urgently needed. Future research should assess workflow integration, human-machine interaction, and, most importantly, the impact on patient outcome. Machine Learning enhanced causal inference for observational data carries an enormous potential to change trauma research as complement to randomized studies. The scientific trauma community needs to engage with the existing challenges to drive progress in the field.
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Affiliation(s)
- Tobias Gauss
- Anesthesia and Critical Care, Grenoble Alpes, University Hospital, Grenoble, France
| | - Zane Perkins
- Centre for Trauma Sciences, Queen Mary University of London, London, UK
| | - Thorsten Tjardes
- Department of Trauma Surgery, Orthopedic Surgery, and Sports Medicine, Cologne Merheim Medical Center, Witten/Herdecke University, Cologne, Germany
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Johns E, Alkanj A, Beck M, Dal Mas L, Gourieux B, Sauleau EA, Michel B. Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review. Eur J Hosp Pharm 2023:ejhpharm-2023-003857. [PMID: 38050067 DOI: 10.1136/ejhpharm-2023-003857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 11/07/2023] [Indexed: 12/06/2023] Open
Abstract
OBJECTIVES The emergence of artificial intelligence (AI) is catching the interest of hospital pharmacists. A massive collection of health data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders. METHODS A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. MEDLINE and Embase databases were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS 13 articles were selected after review: 12 studies were judged to have high risk of bias; 11 studies were published between 2020 and 2023; 8 were conducted in North America and Asia; 6 analysed orders and detected inappropriate prescriptions according to patient profiles and medication orders; and 7 detected specific inappropriate prescriptions, such as detecting antibiotic resistance, dosage abnormality in prescriptions, high alert drugs errors from prescriptions or predicting the risk of adverse drug events. Various AI models were used, mainly supervised learning techniques. The training datasets used were very heterogeneous; the length of study varied from 2 weeks to 7 years and the number of prescription orders analysed went from 31 to 5 804 192. CONCLUSIONS This systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.
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Affiliation(s)
- Erin Johns
- Direction de la Qualité, de la Performance et de l'Innovation, Agence Régionale de Santé Grand Est Site de Strasbourg, Strasbourg, Grand Est, France
- IMAGeS, Laboratoire des Sciences de l'Ingénieur de l'Informatique et de l'Imagerie, Illkirch, Grand Est, France
| | - Ahmad Alkanj
- Laboratoire de Pharmacologie et de Toxicologie Neurocardiovasculaire, Université de Strasbourg, Strasbourg, Grand Est, France
| | - Morgane Beck
- Direction de la Qualité, de la Performance et de l'Innovation, Agence Régionale de Santé Grand Est Site de Strasbourg, Strasbourg, Grand Est, France
| | - Laurent Dal Mas
- Direction de la Qualité, de la Performance et de l'Innovation, Agence Régionale de Santé Grand Est Site de Strasbourg, Strasbourg, Grand Est, France
| | - Benedicte Gourieux
- Laboratoire de Pharmacologie et de Toxicologie Neurocardiovasculaire, Université de Strasbourg, Strasbourg, Grand Est, France
- Service Pharmacie - Stérilisation, Les Hopitaux Universitaires de Strasbourg, Strasbourg, Grand Est, France
| | - Erik-André Sauleau
- IMAGeS, Laboratoire des Sciences de l'Ingénieur de l'Informatique et de l'Imagerie, Illkirch, Grand Est, France
- Département de Santé Publique - Groupe Méthodes Recherche Clinique, Les Hopitaux Universitaires de Strasbourg, Strasbourg, Grand Est, France
| | - Bruno Michel
- Laboratoire de Pharmacologie et de Toxicologie Neurocardiovasculaire, Université de Strasbourg, Strasbourg, Grand Est, France
- Service Pharmacie - Stérilisation, Les Hopitaux Universitaires de Strasbourg, Strasbourg, Grand Est, France
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Susanto AP, Lyell D, Widyantoro B, Berkovsky S, Magrabi F. Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review. J Am Med Inform Assoc 2023; 30:2050-2063. [PMID: 37647865 PMCID: PMC10654852 DOI: 10.1093/jamia/ocad180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/01/2023] [Accepted: 08/23/2023] [Indexed: 09/01/2023] Open
Abstract
OBJECTIVE This study aims to summarize the research literature evaluating machine learning (ML)-based clinical decision support (CDS) systems in healthcare settings. MATERIALS AND METHODS We conducted a review in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta Analyses extension for Scoping Review). Four databases, including PubMed, Medline, Embase, and Scopus were searched for studies published from January 2016 to April 2021 evaluating the use of ML-based CDS in clinical settings. We extracted the study design, care setting, clinical task, CDS task, and ML method. The level of CDS autonomy was examined using a previously published 3-level classification based on the division of clinical tasks between the clinician and CDS; effects on decision-making, care delivery, and patient outcomes were summarized. RESULTS Thirty-two studies evaluating the use of ML-based CDS in clinical settings were identified. All were undertaken in developed countries and largely in secondary and tertiary care settings. The most common clinical tasks supported by ML-based CDS were image recognition and interpretation (n = 12) and risk assessment (n = 9). The majority of studies examined assistive CDS (n = 23) which required clinicians to confirm or approve CDS recommendations for risk assessment in sepsis and for interpreting cancerous lesions in colonoscopy. Effects on decision-making, care delivery, and patient outcomes were mixed. CONCLUSION ML-based CDS are being evaluated in many clinical areas. There remain many opportunities to apply and evaluate effects of ML-based CDS on decision-making, care delivery, and patient outcomes, particularly in resource-constrained settings.
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Affiliation(s)
- Anindya Pradipta Susanto
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Bambang Widyantoro
- Faculty of Medicine, Universitas Indonesia, Jakarta, DKI Jakarta 10430, Indonesia
- National Cardiovascular Center Harapan Kita Hospital, Jakarta, DKI Jakarta 11420, Indonesia
| | - Shlomo Berkovsky
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
| | - Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2109, Australia
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Zsidai B, Hilkert AS, Kaarre J, Narup E, Senorski EH, Grassi A, Ley C, Longo UG, Herbst E, Hirschmann MT, Kopf S, Seil R, Tischer T, Samuelsson K, Feldt R. A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges. J Exp Orthop 2023; 10:117. [PMID: 37968370 PMCID: PMC10651597 DOI: 10.1186/s40634-023-00683-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/21/2023] [Indexed: 11/17/2023] Open
Abstract
Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.
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Affiliation(s)
- Bálint Zsidai
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden.
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Ann-Sophie Hilkert
- Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Medfield Diagnostics AB, Gothenburg, Sweden
| | - Janina Kaarre
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedic Surgery, UPMC Freddie Fu Sports Medicine Center, University of Pittsburgh, Pittsburgh, USA
| | - Eric Narup
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eric Hamrin Senorski
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Sportrehab Sports Medicine Clinic, Gothenburg, Sweden
| | - Alberto Grassi
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- IIa Clinica Ortopedica E Traumatologica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy
| | - Christophe Ley
- Department of Mathematics, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Umile Giuseppe Longo
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University, Rome, Italy
| | - Elmar Herbst
- Department of Trauma, Hand and Reconstructive Surgery, University Hospital Münster, Münster, Germany
| | - Michael T Hirschmann
- Department of Orthopedic Surgery and Traumatology, Head Knee Surgery and DKF Head of Research, Kantonsspital Baselland, 4101, Bruderholz, Switzerland
| | - Sebastian Kopf
- Center of Orthopaedics and Traumatology, University Hospital Brandenburg a.d.H., Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
- Faculty of Health Sciences Brandenburg, Brandenburg Medical School Theodor Fontane, 14770, Brandenburg a.d.H., Germany
| | - Romain Seil
- Department of Orthopaedic Surgery, Centre Hospitalier Luxembourg and Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Thomas Tischer
- Clinic for Orthopaedics and Trauma Surgery, Malteser Waldkrankenhaus St. Marien, Erlangen, Germany
| | - Kristian Samuelsson
- Sahlgrenska Sports Medicine Center, Gothenburg, Sweden
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Orthopaedics, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Robert Feldt
- Department of Orthopaedics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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25
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Ripperger MA, Kolli J, Wilimitis D, Robinson K, Reale C, Novak LL, Cunningham CA, Kasuske LM, Grover SG, Ribeiro JD, Walsh CG. External Validation and Updating of a Statistical Civilian-Based Suicide Risk Model in US Naval Primary Care. JAMA Netw Open 2023; 6:e2342750. [PMID: 37938841 PMCID: PMC10632956 DOI: 10.1001/jamanetworkopen.2023.42750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/29/2023] [Indexed: 11/10/2023] Open
Abstract
Importance Suicide remains an ongoing concern in the US military. Statistical models have not been broadly disseminated for US Navy service members. Objective To externally validate and update a statistical suicide risk model initially developed in a civilian setting with an emphasis on primary care. Design, Setting, and Participants This retrospective cohort study used data collected from 2007 through 2017 among active-duty US Navy service members. The external civilian model was applied to every visit at Naval Medical Center Portsmouth (NMCP), its NMCP Naval Branch Health Clinics (NBHCs), and TRICARE Prime Clinics (TPCs) that fall within the NMCP area. The model was retrained and recalibrated using visits to NBHCs and TPCs and updated using Department of Defense (DoD)-specific billing codes and demographic characteristics, including expanded race and ethnicity categories. Domain and temporal analyses were performed with bootstrap validation. Data analysis was performed from September 2020 to December 2022. Exposure Visit to US NMCP. Main Outcomes and Measures Recorded suicidal behavior on the day of or within 30 days of a visit. Performance was assessed using area under the receiver operating curve (AUROC), area under the precision recall curve (AUPRC), Brier score, and Spiegelhalter z-test statistic. Results Of the 260 583 service members, 6529 (2.5%) had a recorded suicidal behavior, 206 412 (79.2%) were male; 104 835 (40.2%) were aged 20 to 24 years; and 9458 (3.6%) were Asian, 56 715 (21.8%) were Black or African American, and 158 277 (60.7%) were White. Applying the civilian-trained model resulted in an AUROC of 0.77 (95% CI, 0.74-0.79) and an AUPRC of 0.004 (95% CI, 0.003-0.005) at NBHCs with poor calibration (Spiegelhalter P < .001). Retraining the algorithm improved AUROC to 0.92 (95% CI, 0.91-0.93) and AUPRC to 0.66 (95% CI, 0.63-0.68). Number needed to screen in the top risk tiers was 366 for the external model and 200 for the retrained model; the lower number indicates better performance. Domain validation showed AUROC of 0.90 (95% CI, 0.90-0.91) and AUPRC of 0.01 (95% CI, 0.01-0.01), and temporal validation showed AUROC of 0.75 (95% CI, 0.72-0.78) and AUPRC of 0.003 (95% CI, 0.003-0.005). Conclusions and Relevance In this cohort study of active-duty Navy service members, a civilian suicide attempt risk model was externally validated. Retraining and updating with DoD-specific variables improved performance. Domain and temporal validation results were similar to external validation, suggesting that implementing an external model in US Navy primary care clinics may bypass the need for costly internal development and expedite the automation of suicide prevention in these clinics.
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Affiliation(s)
- Michael A. Ripperger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jhansi Kolli
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Drew Wilimitis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Katelyn Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Carrie Reale
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Laurie L. Novak
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Lalon M. Kasuske
- Daniel K. Inouye Graduate School of Nursing, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | | | | | - Colin G. Walsh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee
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26
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Vijayakumar S, Lee VV, Leong QY, Hong SJ, Blasiak A, Ho D. Physicians' Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform. JMIR Hum Factors 2023; 10:e48476. [PMID: 37902825 PMCID: PMC10644191 DOI: 10.2196/48476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 08/24/2023] [Accepted: 09/10/2023] [Indexed: 10/31/2023] Open
Abstract
BACKGROUND Physicians play a key role in integrating new clinical technology into care practices through user feedback and growth propositions to developers of the technology. As physicians are stakeholders involved through the technology iteration process, understanding their roles as users can provide nuanced insights into the workings of these technologies that are being explored. Therefore, understanding physicians' perceptions can be critical toward clinical validation, implementation, and downstream adoption. Given the increasing prevalence of clinical decision support systems (CDSSs), there remains a need to gain an in-depth understanding of physicians' perceptions and expectations toward their downstream implementation. This paper explores physicians' perceptions of integrating CURATE.AI, a novel artificial intelligence (AI)-based and clinical stage personalized dosing CDSSs, into clinical practice. OBJECTIVE This study aims to understand physicians' perspectives of integrating CURATE.AI for clinical work and to gather insights on considerations of the implementation of AI-based CDSS tools. METHODS A total of 12 participants completed semistructured interviews examining their knowledge, experience, attitudes, risks, and future course of the personalized combination therapy dosing platform, CURATE.AI. Interviews were audio recorded, transcribed verbatim, and coded manually. The data were thematically analyzed. RESULTS Overall, 3 broad themes and 9 subthemes were identified through thematic analysis. The themes covered considerations that physicians perceived as significant across various stages of new technology development, including trial, clinical implementation, and mass adoption. CONCLUSIONS The study laid out the various ways physicians interpreted an AI-based personalized dosing CDSS, CURATE.AI, for their clinical practice. The research pointed out that physicians' expectations during the different stages of technology exploration can be nuanced and layered with expectations of implementation that are relevant for technology developers and researchers.
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Affiliation(s)
- Smrithi Vijayakumar
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - V Vien Lee
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Qiao Ying Leong
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - Soo Jung Hong
- Department of Communications and New Media, National University of Singapore, Singapore, Singapore
| | - Agata Blasiak
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Dean Ho
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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27
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Peek N, Sujan M, Scott P. Digital health and care: emerging from pandemic times. BMJ Health Care Inform 2023; 30:e100861. [PMID: 37832967 PMCID: PMC10583078 DOI: 10.1136/bmjhci-2023-100861] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges and opportunities ahead by reviewing significant developments reported in the literature. As before, the three key areas we consider are digital transformation of services, realising the potential of artificial intelligence and wise data sharing to facilitate learning health systems. We conclude that the field of digital health has rapidly matured during the pandemic, but there are still major sociotechnical, evaluation and trust challenges in the development and deployment of new digital services.
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Affiliation(s)
- Niels Peek
- Centre for Health Informatics, The University of Manchester, Manchester, UK
- NIHR Applied Research Collaboration Greater Manchester, The University of Manchester, Manchester, UK
| | - Mark Sujan
- Human Factors Everywhere Ltd, Woking, UK
| | - Philip Scott
- Institute of Management and Health, University of Wales Trinity Saint David, Swansea, UK
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28
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El Naqa I, Karolak A, Luo Y, Folio L, Tarhini AA, Rollison D, Parodi K. Translation of AI into oncology clinical practice. Oncogene 2023; 42:3089-3097. [PMID: 37684407 DOI: 10.1038/s41388-023-02826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Emerging applications of AI/ML in the literature include screening and early detection of cancer, disease diagnosis, response prediction, prognosis, and accelerated drug discovery. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We dissect the unique challenges and opportunities associated with each of these cases. Finally, we summarize the general requirements for successful AI/ML implementation in the clinic, highlighting specific examples and points of emphasis including the importance of multidisciplinary collaboration of stakeholders, role of domain experts in AI augmentation, transparency of AI/ML models, and the establishment of a comprehensive quality assurance program to mitigate risks of training bias and data drifts, all culminating toward safer and more beneficial AI/ML applications in oncology labs and clinics.
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Affiliation(s)
- Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA.
| | - Aleksandra Karolak
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Les Folio
- Diagnostic Imaging & Interventional Radiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Ahmad A Tarhini
- Cutaneous Oncology and Immunology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Dana Rollison
- Department of Cancer Epidemiology, Moffitt Cancer Center, Tampa, FL, 33612, USA
| | - Katia Parodi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Munich, Germany
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29
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Pedrett R, Mascagni P, Beldi G, Padoy N, Lavanchy JL. Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review. Surg Endosc 2023; 37:7412-7424. [PMID: 37584774 PMCID: PMC10520175 DOI: 10.1007/s00464-023-10335-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 07/20/2023] [Indexed: 08/17/2023]
Abstract
BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies.
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Affiliation(s)
- Romina Pedrett
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Pietro Mascagni
- IHU Strasbourg, Strasbourg, France
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Guido Beldi
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Nicolas Padoy
- IHU Strasbourg, Strasbourg, France
- ICube, CNRS, University of Strasbourg, Strasbourg, France
| | - Joël L Lavanchy
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
- IHU Strasbourg, Strasbourg, France.
- University Digestive Health Care Center Basel - Clarunis, PO Box, 4002, Basel, Switzerland.
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30
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Wang SM, Hogg HDJ, Sangvai D, Patel MR, Weissler EH, Kellogg KC, Ratliff W, Balu S, Sendak M. Development and Integration of Machine Learning Algorithm to Identify Peripheral Arterial Disease: Multistakeholder Qualitative Study. JMIR Form Res 2023; 7:e43963. [PMID: 37733427 PMCID: PMC10557008 DOI: 10.2196/43963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 01/20/2023] [Accepted: 04/30/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Machine learning (ML)-driven clinical decision support (CDS) continues to draw wide interest and investment as a means of improving care quality and value, despite mixed real-world implementation outcomes. OBJECTIVE This study aimed to explore the factors that influence the integration of a peripheral arterial disease (PAD) identification algorithm to implement timely guideline-based care. METHODS A total of 12 semistructured interviews were conducted with individuals from 3 stakeholder groups during the first 4 weeks of integration of an ML-driven CDS. The stakeholder groups included technical, administrative, and clinical members of the team interacting with the ML-driven CDS. The ML-driven CDS identified patients with a high probability of having PAD, and these patients were then reviewed by an interdisciplinary team that developed a recommended action plan and sent recommendations to the patient's primary care provider. Pseudonymized transcripts were coded, and thematic analysis was conducted by a multidisciplinary research team. RESULTS Three themes were identified: positive factors translating in silico performance to real-world efficacy, organizational factors and data structure factors affecting clinical impact, and potential challenges to advancing equity. Our study found that the factors that led to successful translation of in silico algorithm performance to real-world impact were largely nontechnical, given adequate efficacy in retrospective validation, including strong clinical leadership, trustworthy workflows, early consideration of end-user needs, and ensuring that the CDS addresses an actionable problem. Negative factors of integration included failure to incorporate the on-the-ground context, the lack of feedback loops, and data silos limiting the ML-driven CDS. The success criteria for each stakeholder group were also characterized to better understand how teams work together to integrate ML-driven CDS and to understand the varying needs across stakeholder groups. CONCLUSIONS Longitudinal and multidisciplinary stakeholder engagement in the development and integration of ML-driven CDS underpins its effective translation into real-world care. Although previous studies have focused on the technical elements of ML-driven CDS, our study demonstrates the importance of including administrative and operational leaders as well as an early consideration of clinicians' needs. Seeing how different stakeholder groups have this more holistic perspective also permits more effective detection of context-driven health care inequities, which are uncovered or exacerbated via ML-driven CDS integration through structural and organizational challenges. Many of the solutions to these inequities lie outside the scope of ML and require coordinated systematic solutions for mitigation to help reduce disparities in the care of patients with PAD.
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Affiliation(s)
- Sabrina M Wang
- Duke University School of Medicine, Durham, NC, United States
| | - H D Jeffry Hogg
- Population Health Science Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle Eye Centre, Royal Victoria Infirmary, Newcastle upon Tyne, United Kingdom
| | - Devdutta Sangvai
- Population Health Management, Duke Health, Durham, NC, United States
| | - Manesh R Patel
- Department of Cardiology, Duke University, Durham, NC, United States
| | - E Hope Weissler
- Department of Vascular Surgery, Duke University, Durham, NC, United States
| | | | - William Ratliff
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Durham, NC, United States
| | - Mark Sendak
- Duke Institute for Health Innovation, Durham, NC, United States
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31
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Klement W, El Emam K. Consolidated Reporting Guidelines for Prognostic and Diagnostic Machine Learning Modeling Studies: Development and Validation. J Med Internet Res 2023; 25:e48763. [PMID: 37651179 PMCID: PMC10502599 DOI: 10.2196/48763] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/11/2023] [Accepted: 07/31/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND The reporting of machine learning (ML) prognostic and diagnostic modeling studies is often inadequate, making it difficult to understand and replicate such studies. To address this issue, multiple consensus and expert reporting guidelines for ML studies have been published. However, these guidelines cover different parts of the analytics lifecycle, and individually, none of them provide a complete set of reporting requirements. OBJECTIVE We aimed to consolidate the ML reporting guidelines and checklists in the literature to provide reporting items for prognostic and diagnostic ML in in-silico and shadow mode studies. METHODS We conducted a literature search that identified 192 unique peer-reviewed English articles that provide guidance and checklists for reporting ML studies. The articles were screened by their title and abstract against a set of 9 inclusion and exclusion criteria. Articles that were filtered through had their quality evaluated by 2 raters using a 9-point checklist constructed from guideline development good practices. The average κ was 0.71 across all quality criteria. The resulting 17 high-quality source papers were defined as having a quality score equal to or higher than the median. The reporting items in these 17 articles were consolidated and screened against a set of 6 inclusion and exclusion criteria. The resulting reporting items were sent to an external group of 11 ML experts for review and updated accordingly. The updated checklist was used to assess the reporting in 6 recent modeling papers in JMIR AI. Feedback from the external review and initial validation efforts was used to improve the reporting items. RESULTS In total, 37 reporting items were identified and grouped into 5 categories based on the stage of the ML project: defining the study details, defining and collecting the data, modeling methodology, model evaluation, and explainability. None of the 17 source articles covered all the reporting items. The study details and data description reporting items were the most common in the source literature, with explainability and methodology guidance (ie, data preparation and model training) having the least coverage. For instance, a median of 75% of the data description reporting items appeared in each of the 17 high-quality source guidelines, but only a median of 33% of the data explainability reporting items appeared. The highest-quality source articles tended to have more items on reporting study details. Other categories of reporting items were not related to the source article quality. We converted the reporting items into a checklist to support more complete reporting. CONCLUSIONS Our findings supported the need for a set of consolidated reporting items, given that existing high-quality guidelines and checklists do not individually provide complete coverage. The consolidated set of reporting items is expected to improve the quality and reproducibility of ML modeling studies.
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Affiliation(s)
- William Klement
- University of Ottawa, Ottawa, ON, Canada
- CHEO Research Institute, Ottawa, ON, Canada
| | - Khaled El Emam
- University of Ottawa, Ottawa, ON, Canada
- CHEO Research Institute, Ottawa, ON, Canada
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van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalke VR, Lane PJ. Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework. J Am Med Inform Assoc 2023; 30:1503-1515. [PMID: 37208863 PMCID: PMC10436156 DOI: 10.1093/jamia/ocad088] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/17/2023] [Accepted: 05/09/2023] [Indexed: 05/21/2023] Open
Abstract
OBJECTIVE To derive a comprehensive implementation framework for clinical AI models within hospitals informed by existing AI frameworks and integrated with reporting standards for clinical AI research. MATERIALS AND METHODS (1) Derive a provisional implementation framework based on the taxonomy of Stead et al and integrated with current reporting standards for AI research: TRIPOD, DECIDE-AI, CONSORT-AI. (2) Undertake a scoping review of published clinical AI implementation frameworks and identify key themes and stages. (3) Perform a gap analysis and refine the framework by incorporating missing items. RESULTS The provisional AI implementation framework, called SALIENT, was mapped to 5 stages common to both the taxonomy and the reporting standards. A scoping review retrieved 20 studies and 247 themes, stages, and subelements were identified. A gap analysis identified 5 new cross-stage themes and 16 new tasks. The final framework comprised 5 stages, 7 elements, and 4 components, including the AI system, data pipeline, human-computer interface, and clinical workflow. DISCUSSION This pragmatic framework resolves gaps in existing stage- and theme-based clinical AI implementation guidance by comprehensively addressing the what (components), when (stages), and how (tasks) of AI implementation, as well as the who (organization) and why (policy domains). By integrating research reporting standards into SALIENT, the framework is grounded in rigorous evaluation methodologies. The framework requires validation as being applicable to real-world studies of deployed AI models. CONCLUSIONS A novel end-to-end framework has been developed for implementing AI within hospital clinical practice that builds on previous AI implementation frameworks and research reporting standards.
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Affiliation(s)
- Anton H van der Vegt
- Centre for Health Services Research, The University of Queensland, Brisbane, Australia
| | - Ian A Scott
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia
| | - Krishna Dermawan
- Centre for Information Resilience, The University of Queensland, St Lucia, Australia
| | - Rudolf J Schnetler
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Queensland Health, Brisbane, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Queensland Health, Brisbane, Australia
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Magrabi F, Lyell D, Coiera E. Automation in Contemporary Clinical Information Systems: a Survey of AI in Healthcare Settings. Yearb Med Inform 2023; 32:115-126. [PMID: 38147855 PMCID: PMC10751141 DOI: 10.1055/s-0043-1768733] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Abstract
AIMS AND OBJECTIVES To examine the nature and use of automation in contemporary clinical information systems by reviewing studies reporting the implementation and evaluation of artificial intelligence (AI) technologies in healthcare settings. METHOD PubMed/MEDLINE, Web of Science, EMBASE, the tables of contents of major informatics journals, and the bibliographies of articles were searched for studies reporting evaluation of AI in clinical settings from January 2021 to December 2022. We documented the clinical application areas and tasks supported, and the level of system autonomy. Reported effects on user experience, decision-making, care delivery and outcomes were summarised. RESULTS AI technologies are being applied in a wide variety of clinical areas. Most contemporary systems utilise deep learning, use routinely collected data, support diagnosis and triage, are assistive (requiring users to confirm or approve AI provided information or decisions), and are used by doctors in acute care settings in high-income nations. AI systems are integrated and used within existing clinical information systems including electronic medical records. There is limited support for One Health goals. Evaluation is largely based on quantitative methods measuring effects on decision-making. CONCLUSION AI systems are being implemented and evaluated in many clinical areas. There remain many opportunities to understand patterns of routine use and evaluate effects on decision-making, care delivery and patient outcomes using mixed-methods. Support for One Health including integrating data about environmental factors and social determinants needs further exploration.
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Affiliation(s)
- Farah Magrabi
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - David Lyell
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
| | - Enrico Coiera
- Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia
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Fu X, Sahai E, Wilkins A. Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response. J Pathol 2023; 260:578-591. [PMID: 37551703 PMCID: PMC10952145 DOI: 10.1002/path.6153] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/25/2023] [Accepted: 06/07/2023] [Indexed: 08/09/2023]
Abstract
In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image types, has begun to radically change how histological images are used in the clinic. Alongside the recognition that the tumour microenvironment (TME) has a profound impact on tumour phenotype, the technical development of highly multiplexed immunofluorescence platforms has enhanced the biological complexity that can be captured in the TME with high precision. AI has an increasingly powerful role in the recognition and quantitation of image features and the association of such features with clinically important outcomes, as occurs in distinct stages in conventional machine learning. Deep-learning algorithms are able to elucidate TME patterns inherent in the input data with minimum levels of human intelligence and, hence, have the potential to achieve clinically relevant predictions and discovery of important TME features. Furthermore, the diverse repertoire of deep-learning algorithms able to interrogate TME patterns extends beyond convolutional neural networks to include attention-based models, graph neural networks, and multimodal models. To date, AI models have largely been evaluated retrospectively, outside the well-established rigour of prospective clinical trials, in part because traditional clinical trial methodology may not always be suitable for the assessment of AI technology. However, to enable digital pathology-based advanced analytics to meaningfully impact clinical care, specific measures of 'added benefit' to the current standard of care and validation in a prospective setting are important. This will need to be accompanied by adequate measures of explainability and interpretability. Despite such challenges, the combination of expanding datasets, increased computational power, and the possibility of integration of pre-clinical experimental insights into model development means there is exciting potential for the future progress of these AI applications. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Xiao Fu
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Biomolecular Modelling LaboratoryThe Francis Crick InstituteLondonUK
| | - Erik Sahai
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
| | - Anna Wilkins
- Tumour Cell Biology LaboratoryThe Francis Crick InstituteLondonUK
- Division of Radiotherapy and ImagingInstitute of Cancer ResearchLondonUK
- Royal Marsden Hospitals NHS TrustLondonUK
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Park SH, Sul AR, Han K, Sung YS. How to Determine If One Diagnostic Method, Such as an Artificial Intelligence Model, is Superior to Another: Beyond Performance Metrics. Korean J Radiol 2023; 24:601-605. [PMID: 37404103 DOI: 10.3348/kjr.2023.0448] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 07/06/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
| | - Ah-Ram Sul
- Division of Healthcare Research Outcomes Research, National Evidence-based Healthcare Collaborating Agency, Seoul, Korea
| | - Kyunghwa Han
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Korea
| | - Yu Sub Sung
- Clinical Research Center, Asan Medical Center, Seoul, Korea
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Planells H, Parmar V, Marcus HJ, Pandit AS. From theory to practice: what is the potential of artificial intelligence in the future of neurosurgery? Expert Rev Neurother 2023; 23:1041-1046. [PMID: 37997765 DOI: 10.1080/14737175.2023.2285432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Affiliation(s)
- Hannah Planells
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Viraj Parmar
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
| | - Hani J Marcus
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
| | - Anand S Pandit
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK
- High-dimensional Neurology, Institute of Neurology, London, UK
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Palacios Gomez M. Human intelligence for authors, reviewers and editors using artificial intelligence. Colomb Med (Cali) 2023; 54:e1005867. [PMID: 38076466 PMCID: PMC10702474 DOI: 10.25100/cm.v54i3.5867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
We call artificial intelligence any machine that processes information with some purpose, complying with the logical rules of Turing's computation described more than 70 years ago. These machines work with instructions called algorithms, a finite and well-defined sequence of information processing implemented by automata (computers) or any digital technology to optimize a process. (2) This means that the purpose of artificial intelligence is optimization.
Optimization is the ability to do or solve something in the most efficient way possible and, in the best case, using the least amount of resources. The intended optimization is programmed and preset by humans; therefore, these technologies are tools humans create for human purposes.
The optimization capability of artificial intelligence is staggering. It is estimated that using artificial intelligence will facilitate the achievement of 134 of the 169 goals agreed in the 2030 Agenda for Sustainable Development. However, in this evaluation, it was projected that it could negatively affect the progress of 59 goals of the same agreement, being social, economic, educational, legal and gender inequality, the phenomenon most affected by artificial intelligence.
This projection shows us that it is necessary to counterbalance the development and implementation of processes mediated by artificial intelligence, to maintain reflection and question the influence of these technological tools, and, above all, to be based on human intelligence. A definition of human intelligence in the data science and artificial intelligence environment would be a collection of contextual tacit knowledge about human values, responsibility, empathy, intuition, or care for another living being that algorithms cannot describe or execute.
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Affiliation(s)
- Mauricio Palacios Gomez
- Editor en jefe de la Revista Colombia Médica, Facultad de salud, Universidad del Valle, Cali, Colombia
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Sujan M, Smith-Frazer C, Malamateniou C, Connor J, Gardner A, Unsworth H, Husain H. Validation framework for the use of AI in healthcare: overview of the new British standard BS30440. BMJ Health Care Inform 2023; 30:e100749. [PMID: 37364922 PMCID: PMC10410839 DOI: 10.1136/bmjhci-2023-100749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/09/2023] [Indexed: 06/28/2023] Open
Affiliation(s)
- Mark Sujan
- Human Factors Everywhere, Woking, UK
- Education, Healthcare Safety Investigation Branch, Reading, UK
| | | | | | | | - Allison Gardner
- National Institute for Health and Care Excellence, London, UK
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van der Vegt AH, Scott IA, Dermawan K, Schnetler RJ, Kalke VR, Lane PJ. Deployment of machine learning algorithms to predict sepsis: systematic review and application of the SALIENT clinical AI implementation framework. J Am Med Inform Assoc 2023:7161075. [PMID: 37172264 DOI: 10.1093/jamia/ocad075] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 04/04/2023] [Accepted: 04/23/2023] [Indexed: 05/14/2023] Open
Abstract
OBJECTIVE To retrieve and appraise studies of deployed artificial intelligence (AI)-based sepsis prediction algorithms using systematic methods, identify implementation barriers, enablers, and key decisions and then map these to a novel end-to-end clinical AI implementation framework. MATERIALS AND METHODS Systematically review studies of clinically applied AI-based sepsis prediction algorithms in regard to methodological quality, deployment and evaluation methods, and outcomes. Identify contextual factors that influence implementation and map these factors to the SALIENT implementation framework. RESULTS The review identified 30 articles of algorithms applied in adult hospital settings, with 5 studies reporting significantly decreased mortality post-implementation. Eight groups of algorithms were identified, each sharing a common algorithm. We identified 14 barriers, 26 enablers, and 22 decision points which were able to be mapped to the 5 stages of the SALIENT implementation framework. DISCUSSION Empirical studies of deployed sepsis prediction algorithms demonstrate their potential for improving care and reducing mortality but reveal persisting gaps in existing implementation guidance. In the examined publications, key decision points reflecting real-word implementation experience could be mapped to the SALIENT framework and, as these decision points appear to be AI-task agnostic, this framework may also be applicable to non-sepsis algorithms. The mapping clarified where and when barriers, enablers, and key decisions arise within the end-to-end AI implementation process. CONCLUSIONS A systematic review of real-world implementation studies of sepsis prediction algorithms was used to validate an end-to-end staged implementation framework that has the ability to account for key factors that warrant attention in ensuring successful deployment, and which extends on previous AI implementation frameworks.
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Affiliation(s)
- Anton H van der Vegt
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Ian A Scott
- Department of Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Australia
| | - Krishna Dermawan
- Centre for Information Resilience, The University of Queensland, St Lucia, Australia
| | - Rudolf J Schnetler
- School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia
| | - Vikrant R Kalke
- Patient Safety and Quality, Clinical Excellence Queensland, Queensland Health, Brisbane, Australia
| | - Paul J Lane
- Safety Quality & Innovation, The Prince Charles Hospital, Queensland Health, Brisbane, Australia
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Fraser AG, Biasin E, Bijnens B, Bruining N, Caiani EG, Cobbaert K, Davies RH, Gilbert SH, Hovestadt L, Kamenjasevic E, Kwade Z, McGauran G, O'Connor G, Vasey B, Rademakers FE. Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives. Expert Rev Med Devices 2023; 20:467-491. [PMID: 37157833 DOI: 10.1080/17434440.2023.2184685] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
INTRODUCTION Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed. AREAS COVERED AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device. The draft EU Regulation on AI proposes rules that will apply across industry sectors, while for devices the Medical Device Regulation also applies. In the CORE-MD project (Coordinating Research and Evidence for Medical Devices), we have surveyed definitions and summarize initiatives made by professional consensus groups, regulators, and standardization bodies. EXPERT OPINION The level of clinical evidence required should be determined according to each application and to legal and methodological factors that contribute to risk, including accountability, transparency, and interpretability. EU guidance for MDSW based on international recommendations does not yet describe the clinical evidence needed for medical AI software. Regulators, notified bodies, manufacturers, clinicians and patients would all benefit from common standards for the clinical evaluation of high-risk AI applications and transparency of their evidence and performance.
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Affiliation(s)
- Alan G Fraser
- University Hospital of Wales, School of Medicine, Cardiff University, Heath Park, Cardiff, U.K
- KU Leuven, Leuven, Belgium
| | | | - Bart Bijnens
- Engineering Sciences, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Nico Bruining
- Department of Clinical and Experimental Information processing (Digital Cardiology), Erasmus Medical Center, Thoraxcenter, Rotterdam, the Netherlands
| | - Enrico G Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | | | - Rhodri H Davies
- Institute of Cardiovascular Science, University College London, London, U.K
| | - Stephen H Gilbert
- Technische Universität Dresden, Else Kröner Fresenius Center for Digital Health, Dresden, Germany
| | | | | | | | | | | | - Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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Wieben AM, Walden RL, Alreshidi BG, Brown SF, Cato K, Coviak CP, Cruz C, D'Agostino F, Douthit BJ, Forbes TH, Gao G, Johnson SG, Lee MA, Mullen-Fortino M, Park JI, Park S, Pruinelli L, Reger A, Role J, Sileo M, Schultz MA, Vyas P, Jeffery AD. Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature. Appl Clin Inform 2023; 14:585-593. [PMID: 37150179 PMCID: PMC10411069 DOI: 10.1055/a-2088-2893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/03/2023] [Indexed: 05/09/2023] Open
Abstract
OBJECTIVES The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.
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Affiliation(s)
- Ann M. Wieben
- University of Wisconsin-Madison School of Nursing, Madison, Wisconsin, United States
| | - Rachel Lane Walden
- Annette and Irwin Eskind Family Biomedical Library, Vanderbilt University, Nashville, Tennessee, United States
| | - Bader G. Alreshidi
- Medical-Surgical Nursing Department, College of Nursing, University of Hail, Hail, Saudi Arabia
| | | | - Kenrick Cato
- Department of Emergency Medicine, Columbia University School of Nursing, New York, New York, United States
| | - Cynthia Peltier Coviak
- Kirkhof College of Nursing, Grand Valley State University, Allendale, Michigan, United States
| | - Christopher Cruz
- Global Health Technology and Informatics, Chevron, San Ramon, California, United States
| | - Fabio D'Agostino
- Department of Medicine and Surgery, Saint Camillus International University of Health Sciences, Rome, Italy
| | - Brian J. Douthit
- Department of Biomedical Informatics, United States Department of Veterans Affairs, Vanderbilt University, Nashville, Tennessee, United States
| | - Thompson H. Forbes
- Department of Advanced Nursing Practice and Education, East Carolina University College of Nursing, Greenville, North Carolina, United States
| | - Grace Gao
- Atlanta VA Quality Scholars Program, Joseph Maxwell Cleland, Atlanta VA Medical Center, North Druid Hills, Georgia, United States
| | - Steve G. Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, United States
| | | | | | - Jung In Park
- Sue and Bill Gross School of Nursing, University of California, Irvine, United States
| | - Suhyun Park
- College of Nursing and College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Lisiane Pruinelli
- College of Nursing and College of Medicine, University of Florida, Gainesville, Florida, United States
| | | | - Jethrone Role
- Loma Linda University Health, Loma Linda, California, United States
| | - Marisa Sileo
- Boston Children's Hospital, Boston, Massachusetts, United States
| | | | - Pankaj Vyas
- University of Arizona College of Nursing, Tucson, Arizona, United States
| | - Alvin D. Jeffery
- U.S. Department of Veterans Affairs, Vanderbilt University School of Nursing, Tennessee Valley Healthcare System, Nashville, Tennessee, United States
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Rees N, Holding K, Sujan M. Information governance as a socio-technical process in the development of trustworthy healthcare AI. FRONTIERS IN COMPUTER SCIENCE 2023. [DOI: 10.3389/fcomp.2023.1134818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023] Open
Abstract
In this paper we describe our experiences of managing information governance (IG) processes for the assurance of healthcare AI, using the example of an out-of-hospital-cardiac-arrest recognition software within the context of the Welsh Ambulance Service. We frame IG as a socio-technical process. IG processes for the development of trustworthy healthcare AI rely on information governance work, which entails dialogue, negotiation, and trade-offs around the legal basis for data sharing, data requirements and data control. Information governance work should start early in the design life cycle and will likely continue throughout. This includes a focus on establishing and building relationships, as well as a focus on organizational readiness and deeper understanding of both AI technologies as well as their safety assurance requirements.
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Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. J Med Internet Res 2023; 25:e43832. [PMID: 36862499 PMCID: PMC10020907 DOI: 10.2196/43832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 01/19/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023] Open
Abstract
BACKGROUND A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.
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Affiliation(s)
- Peng Xue
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyu Si
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dongxu Qin
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bingrui Wei
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Samuel Seery
- Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom
| | - Zichen Ye
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Mingyang Chen
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sumeng Wang
- Department of Cancer Epidemiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cheng Song
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ming Ding
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Wenling Zhang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Anying Bai
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Huijiao Yan
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Le Dang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yuqian Zhao
- Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science & Technology of China, Sichuan, China
| | - Remila Rezhake
- Affiliated Cancer Hospital, The 3rd Affiliated Teaching Hospital of Xinjiang Medical University, Xinjiang, China
| | - Shaokai Zhang
- Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University, Henan, China
| | - Youlin Qiao
- Center for Global Health, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yimin Qu
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Taribagil P, Hogg HDJ, Balaskas K, Keane PA. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. EXPERT REVIEW OF OPHTHALMOLOGY 2023. [DOI: 10.1080/17469899.2023.2175672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Priyal Taribagil
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - HD Jeffry Hogg
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Population Health Science, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Ophthalmology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Road, Newcastle upon Tyne, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
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Park SH, Han K, Jang HY, Park JE, Lee JG, Kim DW, Choi J. Methods for Clinical Evaluation of Artificial Intelligence Algorithms for Medical Diagnosis. Radiology 2023; 306:20-31. [PMID: 36346314 DOI: 10.1148/radiol.220182] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Adequate clinical evaluation of artificial intelligence (AI) algorithms before adoption in practice is critical. Clinical evaluation aims to confirm acceptable AI performance through adequate external testing and confirm the benefits of AI-assisted care compared with conventional care through appropriately designed and conducted studies, for which prospective studies are desirable. This article explains some of the fundamental methodological points that should be considered when designing and appraising the clinical evaluation of AI algorithms for medical diagnosis. The specific topics addressed include the following: (a) the importance of external testing of AI algorithms and strategies for conducting the external testing effectively, (b) the various metrics and graphical methods for evaluating the AI performance as well as essential methodological points to note in using and interpreting them, (c) paired study designs primarily for comparative performance evaluation of conventional and AI-assisted diagnoses, (d) parallel study designs primarily for evaluating the effect of AI intervention with an emphasis on randomized clinical trials, and (e) up-to-date guidelines for reporting clinical studies on AI, with an emphasis on guidelines registered in the EQUATOR Network library. Sound methodological knowledge of these topics will aid the design, execution, reporting, and appraisal of clinical evaluation of AI.
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Affiliation(s)
- Seong Ho Park
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Kyunghwa Han
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Hye Young Jang
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Ji Eun Park
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - June-Goo Lee
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Dong Wook Kim
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
| | - Jaesoon Choi
- From the Department of Radiology and Research Institute of Radiology (S.H.P., J.E.P., D.W.K.) and Department of Biomedical Engineering (J.C.), Asan Medical Center, University of Ulsan College of Medicine, 88, Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, South Korea (K.H.); Department of Radiology, National Cancer Center, Goyang, South Korea (H.Y.J.); and Biomedical Engineering Research Center, Asan Institute for Life Sciences, University of Ulsan College of Medicine, Seoul, South Korea (J.G.L.)
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Djulbegovic B, Hozo I. Medical Decision-Making and Artificial Intelligence. Cancer Treat Res 2023; 189:101-108. [PMID: 37789165 DOI: 10.1007/978-3-031-37993-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
In this chapter, we discuss the potential role that artificial intelligence (AI) may have in medical decision-making, the pros and cons, and the limitations and biases that might be introduced when using these novel techniques. As computing becomes more powerful and models continue to grow increasingly more complex, the potential of AI to improve decision-making is increasingly promising. Within many medical fields, however, at the time of this writing (September 2023), the promise of AI is yet to translate into everyday reality. Here, we summarize the role of AI in medical decision-making (diagnosis, prognosis, and treatment).
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Affiliation(s)
- Benjamin Djulbegovic
- Hematology Stewardship Program, Division of Hematology/Oncology, Department of Medicine, Medical University of South Carolina, Charleston, SC, USA.
| | - Iztok Hozo
- Department of Mathematics, Indiana University Northwest, Gary, IN, USA
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Park SH. Looking Back at 2022 and ahead to 2023 for the Korean Journal of Radiology. Korean J Radiol 2023; 24:15-18. [PMID: 36606615 PMCID: PMC9830144 DOI: 10.3348/kjr.2022.0963] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 12/03/2022] [Indexed: 01/03/2023] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
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King H, Williams B, Treanor D, Randell R. How, for whom, and in what contexts will artificial intelligence be adopted in pathology? A realist interview study. J Am Med Inform Assoc 2022; 30:529-538. [PMID: 36565465 PMCID: PMC9933065 DOI: 10.1093/jamia/ocac254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 11/14/2022] [Accepted: 12/09/2022] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE There is increasing interest in using artificial intelligence (AI) in pathology to improve accuracy and efficiency. Studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting further research is needed regarding integration into clinical practice. This study aimed to explore stakeholders' theories concerning how and in what contexts AI is likely to become integrated into pathology. MATERIALS AND METHODS A literature review provided tentative theories that were revised through a realist interview study with 20 pathologists and 5 pathology trainees. Questions sought to elicit whether, and in what ways, the tentative theories fitted with interviewees' perceptions and experiences. Analysis focused on identifying the contextual factors that may support or constrain uptake of AI in pathology. RESULTS Interviews highlighted the importance of trust in AI, with interviewees emphasizing evaluation and the opportunity for pathologists to become familiar with AI as means for establishing trust. Interviewees expressed a desire to be involved in design and implementation of AI tools, to ensure such tools address pressing needs, but needs vary by subspecialty. Workflow integration is desired but whether AI tools should work automatically will vary according to the task and the context. CONCLUSIONS It must not be assumed that AI tools that provide benefit in one subspecialty will provide benefit in others. Pathologists should be involved in the decision to introduce AI, with opportunity to assess strengths and weaknesses. Further research is needed concerning the evidence required to satisfy pathologists regarding the benefits of AI.
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Affiliation(s)
- Henry King
- School of Medicine, University of Leeds, Leeds, UK
| | - Bethany Williams
- Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Darren Treanor
- School of Medicine, University of Leeds, Leeds, UK,Department of Pathology, Leeds Teaching Hospitals NHS Trust, Leeds, UK,Department of Clinical Pathology, Linköping University, Linköping, Sweden,Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden,Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - Rebecca Randell
- Corresponding Author: Rebecca Randell, PhD, Faculty of Health Studies, University of Bradford, Richmond Road, Bradford BD7 1DP, UK;
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Joyce C, Markossian TW, Nikolaides J, Ramsey E, Thompson HM, Rojas JC, Sharma B, Dligach D, Oguss MK, Cooper RS, Afshar M. The Evaluation of a Clinical Decision Support Tool Using Natural Language Processing to Screen Hospitalized Adults for Unhealthy Substance Use: Protocol for a Quasi-Experimental Design. JMIR Res Protoc 2022; 11:e42971. [PMID: 36534461 PMCID: PMC9808720 DOI: 10.2196/42971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 12/01/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system. OBJECTIVE This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use. METHODS A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models. RESULTS The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025. CONCLUSIONS The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions. TRIAL REGISTRATION ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/42971.
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Affiliation(s)
- Cara Joyce
- Department of Computer Science, Loyola University Chicago, Chicago, IL, United States
| | - Talar W Markossian
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, United States
| | - Jenna Nikolaides
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Elisabeth Ramsey
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Hale M Thompson
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Juan C Rojas
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Brihat Sharma
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Dmitriy Dligach
- Department of Computer Science, Loyola University Chicago, Chicago, IL, United States
| | - Madeline K Oguss
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Richard S Cooper
- Department of Public Health Sciences, Loyola University Chicago, Maywood, IL, United States
| | - Majid Afshar
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
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50
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Park SH, Choi JI, Fournier L, Vasey B. Randomized Clinical Trials of Artificial Intelligence in Medicine: Why, When, and How? Korean J Radiol 2022; 23:1119-1125. [PMID: 36447410 PMCID: PMC9747266 DOI: 10.3348/kjr.2022.0834] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 10/30/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Seong Ho Park
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Joon-Il Choi
- Department of Radiology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Laure Fournier
- Department of Radiology, Université Paris Cité, AP-HP, Hôpital Européen Georges Pompidou, PARCC UMRS 970, INSERM, Paris, France
| | - Baptiste Vasey
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
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