1
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Bowness JS, Liu X, Keane PA. Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example. Br J Anaesth 2024; 132:1016-1021. [PMID: 38302346 DOI: 10.1016/j.bja.2023.12.024] [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: 11/19/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024] Open
Abstract
A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.
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Affiliation(s)
- James S Bowness
- Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Pearse A Keane
- Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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2
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Abbou B, Kessel B, Ben Natan M, Gabbay-Benziv R, Dahan Shriki D, Ophir A, Goldschmid N, Klein A, Roguin A, Dudkiewicz M. When all computers shut down: the clinical impact of a major cyber-attack on a general hospital. Front Digit Health 2024; 6:1321485. [PMID: 38433989 PMCID: PMC10904636 DOI: 10.3389/fdgth.2024.1321485] [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: 10/14/2023] [Accepted: 02/05/2024] [Indexed: 03/05/2024] Open
Abstract
Importance Healthcare organizations operate in a data-rich environment and depend on digital computerized systems; thus, they may be exposed to cyber threats. Indeed, one of the most vulnerable sectors to hacks and malware is healthcare. However, the impact of cyberattacks on healthcare organizations remains under-investigated. Objective This study aims to describe a major attack on an entire medical center that resulted in a complete shutdown of all computer systems and to identify the critical actions required to resume regular operations. Setting This study was conducted on a public, general, and acute care referral university teaching hospital. Methods We report the different recovery measures on various hospital clinical activities and their impact on clinical work. Results The system malfunction of hospital computers did not reduce the number of heart catheterizations, births, or outpatient clinic visits. However, a sharp drop in surgical activities, emergency room visits, and total hospital occupancy was observed immediately and during the first postattack week. A gradual increase in all clinical activities was detected starting in the second week after the attack, with a significant increase of 30% associated with the restoration of the electronic medical records (EMR) and laboratory module and a 50% increase associated with the return of the imaging module archiving. One limitation of the present study is that, due to its retrospective design, there were no data regarding the number of elective internal care hospitalizations that were considered crucial. Conclusions and relevance The risk of ransomware cyberattacks is growing. Healthcare systems at all levels of the hospital should be aware of this threat and implement protocols should this catastrophic event occur. Careful evaluation of steady computer system recovery weekly enables vital hospital function, even under a major cyberattack. The restoration of EMR, laboratory systems, and imaging archiving modules was found to be the most significant factor that allowed the return to normal clinical hospital work.
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Affiliation(s)
- Benyamine Abbou
- Hospital Administration, Hillel Yaffe Medical Center, Hadera, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
| | - Boris Kessel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Surgical Division, Hillel Yaffe Medical Center, Hadera, Israel
| | - Merav Ben Natan
- Pat Matthews Academic School of Nursing, Hillel Yaffe Medical Center, Hadera, Israel
| | - Rinat Gabbay-Benziv
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Division of Obstetrics and Gynecology, Hillel Yaffe Medical Center, Hadera, Israel
| | | | - Anna Ophir
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Surgical Division, Hillel Yaffe Medical Center, Hadera, Israel
| | - Nimrod Goldschmid
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Risk Management Department, Hillel Yaffe Medical Center, Hadera, Israel
| | - Adi Klein
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Division of Pediatrics, Hillel Yaffe Medical Center, Hadera, Israel
| | - Ariel Roguin
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
- Division of Cardiology, Hillel Yaffe Medical Center, Hadera, Israel
| | - Mickey Dudkiewicz
- Hospital Administration, Hillel Yaffe Medical Center, Hadera, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Haifa, Israel
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3
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Thirunavukarasu AJ, Elangovan K, Gutierrez L, Li Y, Tan I, Keane PA, Korot E, Ting DSW. Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial. J Med Internet Res 2023; 25:e49949. [PMID: 37824185 PMCID: PMC10603560 DOI: 10.2196/49949] [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: 06/14/2023] [Revised: 08/21/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023] Open
Abstract
Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.
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Affiliation(s)
- Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Kabilan Elangovan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Yong Li
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Iris Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Edward Korot
- Byers Eye Institute, Stanford University, Palo Alto, CA, United States
- Retina Specialists of Michigan, Grand Rapids, MI, United States
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
- Byers Eye Institute, Stanford University, Palo Alto, CA, United States
- Singapore National Eye Centre, Singapore, Singapore
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4
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Rosen-Zvi M, Frimer M, Shoher A, Liel-Cohen N, Sprecher E, Reuveni MM, Shwarzman D, Onn AT, Voliovitch H. Nurturing next generation physicians: A new Israeli healthtech fellowship. iScience 2023; 26:107550. [PMID: 37744411 PMCID: PMC10517394 DOI: 10.1016/j.isci.2023.107550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023] Open
Abstract
The Israeli Society for HealthTech aims at advancing the integration of innovation and healthcare entrepreneurship into medical practice and across traditional health professions, to benefit patients and improve quality of care. In 2021, the Society launched the first fellowship for board certified physicians in HealthTech. This backstory discusses the motivation of launching the program and reviews the design of the fellowship, including curriculum, the expertise of the lecturers, and initial tangible results of the program.
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Affiliation(s)
- Michal Rosen-Zvi
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, Jerusalem, Israel
| | | | | | - Noah Liel-Cohen
- Israeli Society for HealthTech
- Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel
| | - Eli Sprecher
- Israeli Society for HealthTech
- Division of Dermatology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel and Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel-Aviv University, Tel Aviv, Israel
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5
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Wehkamp K, Krawczak M, Schreiber S. The Quality and Utility of Artificial Intelligence in Patient Care. DEUTSCHES ARZTEBLATT INTERNATIONAL 2023; 120:463-469. [PMID: 37218054 PMCID: PMC10487679 DOI: 10.3238/arztebl.m2023.0124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 11/30/2022] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks. METHODS This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications. RESULTS The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine-a requirement that is often hampered by a lack of transparency. CONCLUSION AI has the potential to improve patient care while meeting the challenge of dealing with an ever-increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific.
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Affiliation(s)
- Kai Wehkamp
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Lübeck, Kiel, Germany
- Department for Medical Management, MSH Medical School Hamburg, Hamburg, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Christian-Albrechts-University of Kiel, University Medical Center Schleswig-Holstein Campus Kiel, Germany
| | - Stefan Schreiber
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Lübeck, Kiel, Germany
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, University Medical Center Schleswig-Holstein Campus Kiel, Germany
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6
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Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol 2023; 78:1216-1233. [PMID: 37208107 DOI: 10.1016/j.jhep.2023.01.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 05/21/2023]
Abstract
Liver transplantation (LT) is a life-saving treatment for individuals with end-stage liver disease. The management of LT recipients is complex, predominantly because of the need to consider demographic, clinical, laboratory, pathology, imaging, and omics data in the development of an appropriate treatment plan. Current methods to collate clinical information are susceptible to some degree of subjectivity; thus, clinical decision-making in LT could benefit from the data-driven approach offered by artificial intelligence (AI). Machine learning and deep learning could be applied in both the pre- and post-LT settings. Some examples of AI applications pre-transplant include optimising transplant candidacy decision-making and donor-recipient matching to reduce waitlist mortality and improve post-transplant outcomes. In the post-LT setting, AI could help guide the management of LT recipients, particularly by predicting patient and graft survival, along with identifying risk factors for disease recurrence and other associated complications. Although AI shows promise in medicine, there are limitations to its clinical deployment which include dataset imbalances for model training, data privacy issues, and a lack of available research practices to benchmark model performance in the real world. Overall, AI tools have the potential to enhance personalised clinical decision-making, especially in the context of liver transplant medicine.
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Affiliation(s)
- Mamatha Bhat
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Division of Gastroenterology & Hepatology, Department of Medicine, University of Toronto, Toronto, ON, Canada.
| | - Madhumitha Rabindranath
- Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Toronto General Hospital Research Institute, University Health Network, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Beatriz Sordi Chara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Douglas A Simonetto
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
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7
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Lareyre F, Behrendt CA, Chaudhuri A, Raffort J. Artificial Intelligence in Vascular Surgical Departments: Slowly But Surely. Angiology 2023; 74:399-400. [PMID: 36042693 DOI: 10.1177/00033197221124759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Fabien Lareyre
- Department of Vascular Surgery, 70607Hospital of Antibes, Juan-les-Pins, France.,Université Côte d'Azur, 477107Inserm U1065, C3M, France
| | - Christian-Alexander Behrendt
- 575329Brandenburg Medical School Theodor-Fontane, Neuruppin, Germany.,Research Group GermanVasc, 06000University Medical Centre Hamburg-Eppendorf, Hamburg, Germany.,Department of Vascular and Endovascular Surgery, Asklepios Clinic Wandsbek, 477107Asklepios Medical School Hamburg, Germany
| | - Arindam Chaudhuri
- Bedfordshire - Milton Keynes Vascular Centre, 575329Bedfordshire Hospitals NHS Foundation Trust, UK
| | - Juliette Raffort
- Université Côte d'Azur, 477107Inserm U1065, C3M, France.,Department of Clinical Biochemistry, University Hospital of Nice, France.,3IA Institute, Université Côte d'Azur, Sophia Antipolis, France
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8
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van der Laan JJH, van der Putten JA, Zhao X, Karrenbeld A, Peters FTM, Westerhof J, de With PHN, van der Sommen F, Nagengast WB. Optical Biopsy of Dysplasia in Barrett's Oesophagus Assisted by Artificial Intelligence. Cancers (Basel) 2023; 15:cancers15071950. [PMID: 37046611 PMCID: PMC10093622 DOI: 10.3390/cancers15071950] [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: 02/03/2023] [Revised: 03/17/2023] [Accepted: 03/21/2023] [Indexed: 04/14/2023] Open
Abstract
Optical biopsy in Barrett's oesophagus (BE) using endocytoscopy (EC) could optimize endoscopic screening. However, the identification of dysplasia is challenging due to the complex interpretation of the highly detailed images. Therefore, we assessed whether using artificial intelligence (AI) as second assessor could help gastroenterologists in interpreting endocytoscopic BE images. First, we prospectively videotaped 52 BE patients with EC. Then we trained and tested the AI pm distinct datasets drawn from 83,277 frames, developed an endocytoscopic BE classification system, and designed online training and testing modules. We invited two successive cohorts for these online modules: 10 endoscopists to validate the classification system and 12 gastroenterologists to evaluate AI as second assessor by providing six of them with the option to request AI assistance. Training the endoscopists in the classification system established an improved sensitivity of 90.0% (+32.67%, p < 0.001) and an accuracy of 77.67% (+13.0%, p = 0.020) compared with the baseline. However, these values deteriorated at follow-up (-16.67%, p < 0.001 and -8.0%, p = 0.009). Contrastingly, AI-assisted gastroenterologists maintained high sensitivity and accuracy at follow-up, subsequently outperforming the unassisted gastroenterologists (+20.0%, p = 0.025 and +12.22%, p = 0.05). Thus, best diagnostic scores for the identification of dysplasia emerged through human-machine collaboration between trained gastroenterologists with AI as the second assessor. Therefore, AI could support clinical implementation of optical biopsies through EC.
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Affiliation(s)
- Jouke J H van der Laan
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Joost A van der Putten
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Xiaojuan Zhao
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Arend Karrenbeld
- Department of Pathology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Frans T M Peters
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Jessie Westerhof
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding and Architectures, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
| | - Wouter B Nagengast
- Department of Gastroenterology and Hepatology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands
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9
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Szabo L, Raisi-Estabragh Z, Salih A, McCracken C, Ruiz Pujadas E, Gkontra P, Kiss M, Maurovich-Horvath P, Vago H, Merkely B, Lee AM, Lekadir K, Petersen SE. Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging. Front Cardiovasc Med 2022; 9:1016032. [PMID: 36426221 PMCID: PMC9681217 DOI: 10.3389/fcvm.2022.1016032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 10/11/2022] [Indexed: 12/01/2023] Open
Abstract
A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.
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Affiliation(s)
- Liliana Szabo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Zahra Raisi-Estabragh
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Ahmed Salih
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Celeste McCracken
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, University of Oxford, Oxford, United Kingdom
| | - Esmeralda Ruiz Pujadas
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Polyxeni Gkontra
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Mate Kiss
- Siemens Healthcare Hungary, Budapest, Hungary
| | - Pal Maurovich-Horvath
- Department of Radiology, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Hajnalka Vago
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Bela Merkely
- Semmelweis University Heart and Vascular Center, Budapest, Hungary
| | - Aaron M. Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
| | - Karim Lekadir
- Departament de Matemàtiques i Informàtica, Artificial Intelligence in Medicine Lab (BCN-AIM), Universitat de Barcelona, Barcelona, Spain
| | - Steffen E. Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, United Kingdom
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom
- Health Data Research UK, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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10
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Mendonca EA, Richesson RL, Hochheiser H, Cooper DM, Bruck MN, Berner ES. Informatics education for translational research teams: An unrealized opportunity to strengthen the national research infrastructure. J Clin Transl Sci 2022; 6:e130. [PMID: 36590353 PMCID: PMC9794970 DOI: 10.1017/cts.2022.481] [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: 06/01/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 12/28/2022] Open
Abstract
Objective To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics. Introduction Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education. Methods We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences. Results Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents. Discussion Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.
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Affiliation(s)
- Eneida A. Mendonca
- Indiana University/Regenstrief Institute, Indianapolis, IN, USA
- Cincinnati Children’s Hospital and University of Cincinnati, Cincinnati, OH, USA
| | | | | | | | - Meg N. Bruck
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eta S. Berner
- University of Alabama at Birmingham, Birmingham, AL, USA
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11
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Practical Application of Artificial Intelligence Technology in Glaucoma Diagnosis. J Ophthalmol 2022; 2022:5212128. [PMID: 35957747 PMCID: PMC9357716 DOI: 10.1155/2022/5212128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/29/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose. By comparing the performance of different models between artificial intelligence (AI) and doctors, we aim to evaluate and identify the optimal model for future usage of AI. Methods. A total of 500 fundus images of glaucoma and 500 fundus images of normal eyes were collected and randomly divided into five groups, with each group corresponding to one round. The AI system provided diagnostic suggestions for each image. Four doctors provided diagnoses without the assistance of the AI in the first round and with the assistance of the AI in the second and third rounds. In the fourth round, doctor B and doctor D made diagnoses with the help of the AI and the other two doctors without the help of the AI. In the last round, doctor A and doctor B made diagnoses with the help of AI and the other two doctors without the help of the AI. Results. Doctor A, doctor B, and doctor D had a higher accuracy in the diagnosis of glaucoma with the assistance of AI in the second (
,
, and
) and the third round (
,
, and
) than in the first round. The accuracy of at least one doctor was higher than that of AI in the second and third rounds, in spite of no detectable significance (
,
,
, and
). The four doctors’ overall accuracy (
and
) and sensitivity (
and
) as a whole were significantly improved in the second and third rounds. Conclusions. This “Doctor + AI” model can clarify the role of doctors and AI in medical responsibility and ensure the safety of patients, and importantly, this model shows great potential and application prospects.
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12
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Nath S, Marie A, Ellershaw S, Korot E, Keane PA. New meaning for NLP: the trials and tribulations of natural language processing with GPT-3 in ophthalmology. Br J Ophthalmol 2022; 106:889-892. [PMID: 35523534 DOI: 10.1136/bjophthalmol-2022-321141] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 04/25/2022] [Indexed: 11/04/2022]
Abstract
Natural language processing (NLP) is a subfield of machine intelligence focused on the interaction of human language with computer systems. NLP has recently been discussed in the mainstream media and the literature with the advent of Generative Pre-trained Transformer 3 (GPT-3), a language model capable of producing human-like text. The release of GPT-3 has also sparked renewed interest on the applicability of NLP to contemporary healthcare problems. This article provides an overview of NLP models, with a focus on GPT-3, as well as discussion of applications specific to ophthalmology. We also outline the limitations of GPT-3 and the challenges with its integration into routine ophthalmic care.
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Affiliation(s)
- Siddharth Nath
- Ophthalmology and Visual Sciences, McGill University, Montreal, Quebec, Canada.,National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK
| | - Abdullah Marie
- School of Medicine and Dentistry, Queen's University Belfast, Belfast, UK
| | - Simon Ellershaw
- UKRI Centre for Doctoral Training in AI-enabled Healthcare, University College London, London, UK
| | - Edward Korot
- Byers Eye Institute, Stanford University, Stanford, California, USA
| | - Pearse A Keane
- National Institute for Health Research, Biomedical Research Centre for Ophthalmology, UCL Institute of Ophthalmology, Moorfields Eye Hospital City Road Campus, London, UK
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13
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Dow ER, Keenan TDL, Lad EM, Lee AY, Lee CS, Loewenstein A, Eydelman MB, Chew EY, Keane PA, Lim JI. From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration. Ophthalmology 2022; 129:e43-e59. [PMID: 35016892 PMCID: PMC9859710 DOI: 10.1016/j.ophtha.2022.01.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/16/2021] [Accepted: 01/04/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus. RESULTS Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization. CONCLUSIONS Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.
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Affiliation(s)
- Eliot R Dow
- Byers Eye Institute, Stanford University, Palo Alto, California
| | - Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Malvina B Eydelman
- Office of Health Technology 1, Center of Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.
| | - Jennifer I Lim
- Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
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14
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Traynor OJ, O’Connell PR. The Past, Present and Future of Surgical Education in Ireland. Indian J Surg 2022. [DOI: 10.1007/s12262-021-02988-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
AbstractThe Royal College of Surgeons in Ireland (RCSI) was founded primarily to advance the standards of surgical training and surgical practice in Ireland. Over the course of almost 240 years, the College evolved as a comprehensive University of Medicine and Health Sciences, delivering education not only in Surgery but also in Medicine, Physiotherapy, Pharmacy and Nursing. RCSI today has a large global footprint and runs healthcare education programmes in several international locations. The College works in close collaboration with the other surgical Royal Colleges in Great Britain and the four colleges have a common surgical curriculum. RCSI recognises the increasing challenges facing traditional surgical training models and therefore has developed structured education programmes to support the conventional apprenticeship model. Surgical training in Ireland includes comprehensive training in both technical (operative) skills and non-technical (human factors) skills. RCSI is committed to the integration of simulation in surgical training and has invested heavily in state-of-the-art simulation facilities and in next-generation education technologies such as virtual reality, augmented reality and Artificial Intelligence-based educational technologies. Formal surgical training programmes in Ireland are complemented by a range of Masters programmes in Surgical Science and Practice and also Human Factors in Patient Safety.
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15
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van Heijst LE, Zhao X, Gabriëls RY, Nagengast WB. Today’s Mistakes and Tomorrow’s Wisdom in Endoscopic Imaging of Barrett’s Esophagus. Visc Med 2022; 38:182-188. [DOI: 10.1159/000523907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 02/08/2022] [Indexed: 11/19/2022] Open
Abstract
<b><i>Background:</i></b> Esophageal adenocarcinoma (EAC) is one of the main causes of cancer-related deaths worldwide and its incidence is rising. Barrett’s esophagus (BE) can develop low- and high-grade dysplasia which can progress to EAC overtime. The golden standard to detect dysplastic BE (DBE) or EAC is surveillance with high-definition white-light endoscopy (HD-WLE) and random biopsies according to the Seattle protocol. However, this method is time-consuming and associated with a remarkable miss rate. Therefore, there is great need for the development of novel reliable techniques to optimize surveillance strategies and improve detection rates. <b><i>Summary:</i></b> Optical chromoendoscopy (OC) techniques like narrow-band imaging have shown improved detection of DBE and EAC compared to HD-WLE and random biopsies. Most recent OC techniques, including the iSCAN optical enhancement system and linked color imaging, showed improved characterization of DBE and EAC retrospectively. Fluorescence molecular endoscopy (FME) presented promising results to highlight DBE and EAC. Moreover, with the establishment of well-performing delineation computer-aided detection (CAD) algorithms and the first real-time CAD system for EAC, we expect clinical application of CAD in the near future. <b><i>Key Messages:</i></b> Despite impressive progress made in the development of advanced endoscopic techniques, combined HD-WLE/OC followed by random biopsies remains the golden standard for BE surveillance. Surveillance depends on appropriate mucosal cleansing, sufficient inspection time, and competence of the performing gastroenterologist to improve detection of EAC. In addition, to facilitate the clinical implementation of advanced endoscopic techniques, multicenter prospective clinical studies are demanded for OC and FME. Meanwhile, further optimization of CAD algorithms, the education of gastroenterologists, and analysis of the interaction between the clinician and the computer should be performed.
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16
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Mikdadi D, O'Connell KA, Meacham PJ, Dugan MA, Ojiere MO, Carlson TB, Klenk JA. Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomark 2022; 33:173-184. [PMID: 35213360 DOI: 10.3233/cbm-210301] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery. OBJECTIVE We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations. METHODS We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers. RESULTS Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers. CONCLUSIONS Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.
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Affiliation(s)
- Dina Mikdadi
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Kyle A O'Connell
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA.,Department of Biology, George Washington University, Washington, DC, USA
| | - Philip J Meacham
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Madeleine A Dugan
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Michael O Ojiere
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Thaddeus B Carlson
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Juergen A Klenk
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
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17
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van de Sande D, Van Genderen ME, Smit JM, Huiskens J, Visser JJ, Veen RER, van Unen E, Ba OH, Gommers D, Bommel JV. Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter. BMJ Health Care Inform 2022; 29:bmjhci-2021-100495. [PMID: 35185012 PMCID: PMC8860016 DOI: 10.1136/bmjhci-2021-100495] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 01/24/2022] [Indexed: 12/23/2022] Open
Abstract
Objective Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians’ understanding and to promote quality of medical AI research. Methods We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine. Conclusion This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.
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Affiliation(s)
- Davy van de Sande
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Michel E Van Genderen
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jim M Smit
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands.,Pattern Recognition and Bioinformatics group, EEMCS, Delft University of Technology, Delft, The Netherlands
| | | | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands.,Department of Information Technology, Chief Medical Information Officer, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Robert E R Veen
- Department of Information Technology, theme Research Suite, Erasmus Medical Center, Rotterdam, The Netherlands
| | | | - Oliver Hilgers Ba
- Active Medical Devices/Medical Device Software, CE Plus GmbH, Badenweiler, Germany
| | - Diederik Gommers
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Jasper van Bommel
- Department of Adult Intensive Care, Erasmus Medical Center, Rotterdam, The Netherlands
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18
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Perrier E, Rifai M, Terzic A, Dubois C, Cohen JF. Knowledge, attitudes, and practices towards artificial intelligence among young pediatricians: A nationwide survey in France. Front Pediatr 2022; 10:1065957. [PMID: 36619510 PMCID: PMC9816325 DOI: 10.3389/fped.2022.1065957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To assess the knowledge, attitudes, and practices (KAP) towards artificial intelligence (AI) among young pediatricians in France. METHODS We invited young French pediatricians to participate in an online survey. Invitees were identified through various email listings and social media. We conducted a descriptive analysis and explored whether survey responses varied according to respondents' previous training in AI and level of clinical experience (i.e., residents vs. experienced doctors). RESULTS In total, 165 French pediatricians participated in the study (median age 27 years, women 78%, residents 64%). While 90% of participants declared they understood the term "artificial intelligence", only 40% understood the term "deep learning". Most participants expected AI would lead to improvements in healthcare (e.g., better access to healthcare, 80%; diagnostic assistance, 71%), and 86% declared they would favor implementing AI tools in pediatrics. Fifty-nine percent of respondents declared seeing AI as a threat to medical data security and 35% as a threat to the ethical and human dimensions of medicine. Thirty-nine percent of respondents feared losing clinical skills because of AI, and 6% feared losing their job because of AI. Only 5% of respondents had received specific training in AI, while 87% considered implementing such programs would be necessary. Respondents who received training in AI had significantly better knowledge and a higher probability of having encountered AI tools in their medical practice (p < 0.05 for both). There was no statistically significant difference between residents' and experienced doctors' responses. CONCLUSION In this survey, most young French pediatricians had favorable views toward AI, but a large proportion expressed concerns regarding the ethical, societal, and professional issues linked with the implementation of AI.
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Affiliation(s)
- Emma Perrier
- Child Neurological Rehabilitation Unit and Learning Disorders Reference Centre, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Mahmoud Rifai
- Pediatric Intensive Care Unit, Assistance Publique-Hôpitaux de Paris, Hôpital Raymond-Poincaré, Université Paris-Saclay, Paris, France
| | - Arnaud Terzic
- Pediatric Intensive Care and Neonatal Medicine, Assistance Publique - Hôpitaux de Paris, Hôpital Bicêtre, Université Paris-Saclay, Le Kremlin-Bicêtre, France
| | - Constance Dubois
- Centre of Research in Epidemiology and Statistics, Inserm UMR 1153, Université Paris Cité, Paris, France
| | - Jérémie F Cohen
- Centre of Research in Epidemiology and Statistics, Inserm UMR 1153, Université Paris Cité, Paris, France.,Department of General Pediatrics and Pediatric Infectious Disease, Assistance Publique - Hôpitaux de Paris, Hôpital Necker - Enfants Malades, Université Paris Cité, Paris, France
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19
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Niki O, Saira G, Arvind S, Mike D. Cyber-attacks are a permanent and substantial threat to health systems: Education must reflect that. Digit Health 2022; 8:20552076221104665. [PMID: 35746951 PMCID: PMC9210086 DOI: 10.1177/20552076221104665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 05/14/2022] [Indexed: 11/30/2022] Open
Abstract
Cyber-attacks on healthcare institutions have increased in recent years and have made headlines through the COVID-19 pandemic. With the fallout of attacks increasingly reported in academic research and in the media, there is a real urgency to address cyber-threats that must be augmented across and within health systems. Until now, clinical healthcare professionals have considered cyber-attacks on healthcare organisations a predominantly information and communication technology issue, but this perception is no longer fit-for-purpose. This commentary provides insights into the scale of cyber-attacks and their impact on staff wellbeing, arguing that cybersecurity education for all staff in healthcare organisations must be improved through online resources, simulation, and gaming. The role of national educators, policymakers, and multilateral organisations in achieving this is outlined alongside implications for future policy and practice.
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Affiliation(s)
- O’Brien Niki
- Institute of Global Health Innovation, Imperial College London, London, UK
| | - Ghafur Saira
- Institute of Global Health Innovation, Imperial College London, London, UK
| | | | - Durkin Mike
- Institute of Global Health Innovation, Imperial College London, London, UK
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20
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O'Byrne C, Abbas A, Korot E, Keane PA. Automated deep learning in ophthalmology: AI that can build AI. Curr Opin Ophthalmol 2021; 32:406-412. [PMID: 34231529 DOI: 10.1097/icu.0000000000000779] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
PURPOSE OF REVIEW The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning. RECENT FINDINGS There are several commercially available automated deep learning platforms. Although specific features differ between platforms, they utilise the common approach of supervised learning. Ophthalmology is an exemplar speciality in the area, with a number of recent proof-of-concept studies exploring classification of retinal fundus photographs, optical coherence tomography images and indocyanine green angiography images. Automated deep learning has also demonstrated impressive results in other specialities such as dermatology, radiology and histopathology. SUMMARY Automated deep learning allows users without coding expertise to develop deep learning algorithms. It is rapidly establishing itself as a valuable tool for those with limited technical experience. Despite residual challenges, it offers considerable potential in the future of patient management, clinical research and medical education. VIDEO ABSTRACT http://links.lww.com/COOP/A44.
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Affiliation(s)
- Ciara O'Byrne
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Trinity College School of Medicine, Dublin, Ireland
| | - Abdallah Abbas
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- University College London Medical School, London, UK
| | - Edward Korot
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Byers Eye Institute, Stanford University, Stanford, California, USA
| | - Pearse A Keane
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation, London, UK
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21
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Korot E, Gonçalves MB, Khan SM, Struyven R, Wagner SK, Keane PA. Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization. Curr Opin Ophthalmol 2021; 32:445-451. [PMID: 34265784 DOI: 10.1097/icu.0000000000000785] [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] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology. RECENT FINDINGS Open datasets, efficient labeling techniques, code-free automated machine learning (AutoML) and cloud-based platforms for deployment are resources that enable clinicians with scarce resources to drive their own AI projects. SUMMARY Clinicians are the use-case experts who are best suited to drive AI projects tackling patient-relevant outcome measures. Taken together, open datasets, efficient labeling techniques, code-free AutoML and cloud platforms break the barriers for clinician-driven AI. As AI becomes increasingly democratized through such tools, clinicians and patients stand to benefit greatly.
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Affiliation(s)
- Edward Korot
- Stanford University Byers Eye Institute, Palo Alto, California, USA
- Moorfields Eye Hospital, London, UK
| | - Mariana B Gonçalves
- Moorfields Eye Hospital, London, UK
- Federal University of São Paulo (UNIFESP)
- Vision Institute (IPEPO), Sao Paulo, Brazil
| | | | - Robbert Struyven
- Moorfields Eye Hospital, London, UK
- University College London, London, UK
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22
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Hussain R. Big data, medicines safety and pharmacovigilance. J Pharm Policy Pract 2021; 14:48. [PMID: 34078480 PMCID: PMC8170061 DOI: 10.1186/s40545-021-00329-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 05/26/2021] [Indexed: 11/24/2022] Open
Affiliation(s)
- Rabia Hussain
- Faculty of Pharmacy, The University of Lahore, Lahore, 54000, Pakistan.
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