1
|
Teotia K, Jia Y, Link Woite N, Celi LA, Matos J, Struja T. Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations. J Biomed Inform 2024; 153:104643. [PMID: 38621640 DOI: 10.1016/j.jbi.2024.104643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 03/29/2024] [Accepted: 04/12/2024] [Indexed: 04/17/2024]
Abstract
OBJECTIVE Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). METHODS Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. RESULTS We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. CONCLUSION We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.
Collapse
Affiliation(s)
- Khushboo Teotia
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yueran Jia
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Naira Link Woite
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
| | - João Matos
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Faculty of Engineering, University of Porto (FEUP), Porto, Portugal; Institute for Systems and Computer Engineering, Technology and Science (INESCTEC), Porto, Portugal.
| | - Tristan Struja
- Laboratory for Computational Physiology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Medical University Clinic, Kantonsspital Aarau, Aarau, Switzerland.
| |
Collapse
|
2
|
Pressman SM, Borna S, Gomez-Cabello CA, Haider SA, Haider C, Forte AJ. AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research. Healthcare (Basel) 2024; 12:825. [PMID: 38667587 PMCID: PMC11050155 DOI: 10.3390/healthcare12080825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 04/02/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
INTRODUCTION As large language models receive greater attention in medical research, the investigation of ethical considerations is warranted. This review aims to explore surgery literature to identify ethical concerns surrounding these artificial intelligence models and evaluate how autonomy, beneficence, nonmaleficence, and justice are represented within these ethical discussions to provide insights in order to guide further research and practice. METHODS A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Five electronic databases were searched in October 2023. Eligible studies included surgery-related articles that focused on large language models and contained adequate ethical discussion. Study details, including specialty and ethical concerns, were collected. RESULTS The literature search yielded 1179 articles, with 53 meeting the inclusion criteria. Plastic surgery, orthopedic surgery, and neurosurgery were the most represented surgical specialties. Autonomy was the most explicitly cited ethical principle. The most frequently discussed ethical concern was accuracy (n = 45, 84.9%), followed by bias, patient confidentiality, and responsibility. CONCLUSION The ethical implications of using large language models in surgery are complex and evolving. The integration of these models into surgery necessitates continuous ethical discourse to ensure responsible and ethical use, balancing technological advancement with human dignity and safety.
Collapse
Affiliation(s)
| | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - Syed A. Haider
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Clifton Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA
| | - Antonio J. Forte
- Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Digital Health, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
3
|
Droppelmann G, Rodríguez C, Jorquera C, Feijoo F. Artificial intelligence in diagnosing upper limb musculoskeletal disorders: a systematic review and meta-analysis of diagnostic tests. EFORT Open Rev 2024; 9:241-251. [PMID: 38579757 PMCID: PMC11044087 DOI: 10.1530/eor-23-0174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/07/2024] Open
Abstract
Purpose The integration of artificial intelligence (AI) in radiology has revolutionized diagnostics, optimizing precision and decision-making. Specifically in musculoskeletal imaging, AI tools can improve accuracy for upper extremity pathologies. This study aimed to assess the diagnostic performance of AI models in detecting musculoskeletal pathologies of the upper extremity using different imaging modalities. Methods A meta-analysis was conducted, involving searches on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The quality of the studies was assessed using the QUADAS-2 tool. Diagnostic accuracy measures including sensitivity, specificity, diagnostic odds ratio (DOR), positive and negative likelihood ratios (PLR, NLR), area under the curve (AUC), and summary receiver operating characteristic were pooled using a random-effects model. Heterogeneity and subgroup analyses were also included. All statistical analyses and plots were performed using the R software package. Results Thirteen models from ten articles were analyzed. The sensitivity and specificity of the AI models to detect musculoskeletal conditions in the upper extremity were 0.926 (95% CI: 0.900; 0.945) and 0.908 (95% CI: 0.810; 0.958). The PLR, NLR, lnDOR, and the AUC estimates were found to be 19.18 (95% CI: 8.90; 29.34), 0.11 (95% CI: 0.18; 0.46), 4.62 (95% CI: 4.02; 5.22) with a (P < 0.001), and 95%, respectively. Conclusion The AI models exhibited strong univariate and bivariate performance in detecting both positive and negative cases within the analyzed dataset of musculoskeletal pathologies in the upper extremity.
Collapse
Affiliation(s)
- Guillermo Droppelmann
- Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, RM, Chile
- Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | | | - Carlos Jorquera
- Facultad de Ciencias, Escuela de Nutrición y Dietética, Universidad Mayor, Santiago, RM, Chile
| | - Felipe Feijoo
- School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile
| |
Collapse
|
4
|
Montomoli J, Bitondo MM, Cascella M, Rezoagli E, Romeo L, Bellini V, Semeraro F, Gamberini E, Frontoni E, Agnoletti V, Altini M, Benanti P, Bignami EG. Algor-ethics: charting the ethical path for AI in critical care. J Clin Monit Comput 2024:10.1007/s10877-024-01157-y. [PMID: 38573370 DOI: 10.1007/s10877-024-01157-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.
Collapse
Affiliation(s)
- Jonathan Montomoli
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy.
- Health Services Research, Evaluation and Policy Unit, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy.
| | - Maria Maddalena Bitondo
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy
| | - Marco Cascella
- Unit of Anesthesia and Pain Medicine, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana, " University of Salerno, Baronissi, Salerno, Italy
| | - Emanuele Rezoagli
- School of Medicine and Surgery, University of Milano-Bicocca, Via Cadore, 48, Monza, 20900, Italy
- Dipartimento di Emergenza e Urgenza, Terapia intensiva e Semintensiva adulti e pediatrica, Fondazione IRCCS San Gerardo dei Tintori, Via Pergolesi, 33, Monza, 20900, Italy
| | - Luca Romeo
- Department of Economics and Law, University of Macerata, Macerata, 62100, Italy
| | - Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43125, Italy
| | - Federico Semeraro
- Department of Anesthesia, Intensive Care and Prehospital Emergency, Ospedale Maggiore Carlo Alberto Pizzardi, Largo Bartolo Nigrisoli, 2, Bologna, 40133, Italy
| | - Emiliano Gamberini
- Department of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Viale Settembrini 2, Rimini, 47923, Italy
| | - Emanuele Frontoni
- Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, 62100, Italy
| | - Vanni Agnoletti
- Department of Surgery and Trauma, Anesthesia and Intensive Care Unit, Maurizio Bufalini Hospital, Romagna Local Health Authority, Viale Giovanni Ghirotti, 286, Cesena, 47521, Italy
| | - Mattia Altini
- Hospital Care Sector, Emilia-Romagna Region, Via Aldo Moro, 21, Bologna, 40127, Italy
| | - Paolo Benanti
- Pontifical Gregorian University, Piazza della Pilotta 4, Roma, 00187, Italy
| | - Elena Giovanna Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Via Gramsci 14, Parma, 43125, Italy
| |
Collapse
|
5
|
Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
Collapse
Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| |
Collapse
|
6
|
Ranard BL, Park S, Jia Y, Zhang Y, Alwan F, Celi LA, Lusczek ER. Minimizing bias when using artificial intelligence in critical care medicine. J Crit Care 2024; 82:154796. [PMID: 38552451 DOI: 10.1016/j.jcrc.2024.154796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/02/2024] [Accepted: 03/06/2024] [Indexed: 04/02/2024]
Affiliation(s)
- Benjamin L Ranard
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons and NewYork-Presbyterian Hospital, New York, NY, USA; Program for Hospital and Intensive Care Informatics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA.
| | - Soojin Park
- Program for Hospital and Intensive Care Informatics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; Departments of Neurology and Bioinformatics, Columbia University Vagelos College of Physicians and Surgeons and NewYork-Presbyterian Hospital, New York, NY, USA
| | - Yugang Jia
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Fatima Alwan
- Department of Surgery, Hennepin Healthcare, Minneapolis, MN, USA
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, USA; Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Elizabeth R Lusczek
- Department of Surgery, University of Minnesota Department of Surgery, Minneapolis, MN, USA.
| |
Collapse
|
7
|
Cevik J, Lim B, Seth I, Sofiadellis F, Ross RJ, Cuomo R, Rozen WM. Assessment of the bias of artificial intelligence generated images and large language models on their depiction of a surgeon. ANZ J Surg 2024; 94:287-294. [PMID: 38087912 DOI: 10.1111/ans.18792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/22/2023] [Accepted: 11/12/2023] [Indexed: 03/20/2024]
Affiliation(s)
- Jevan Cevik
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- The Alfred Centre, Central Clinical School at Monash University, 99 Commercial Rd, Melbourne, Victoria, 3004, Australia
| | - Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- The Alfred Centre, Central Clinical School at Monash University, 99 Commercial Rd, Melbourne, Victoria, 3004, Australia
| | - Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- The Alfred Centre, Central Clinical School at Monash University, 99 Commercial Rd, Melbourne, Victoria, 3004, Australia
| | - Foti Sofiadellis
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Richard J Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, 53100, Italy
| | - Warren M Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia
- The Alfred Centre, Central Clinical School at Monash University, 99 Commercial Rd, Melbourne, Victoria, 3004, Australia
| |
Collapse
|
8
|
Ng JY, Cramer H, Lee MS, Moher D. Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Integr Med Res 2024; 13:101024. [PMID: 38384497 PMCID: PMC10879672 DOI: 10.1016/j.imr.2024.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/02/2024] [Accepted: 02/07/2024] [Indexed: 02/23/2024] Open
Abstract
The convergence of traditional, complementary, and integrative medicine (TCIM) with artificial intelligence (AI) is a promising frontier in healthcare. TCIM is a patient-centric approach that combines conventional medicine with complementary therapies, emphasizing holistic well-being. AI can revolutionize healthcare through data-driven decision-making and personalized treatment plans. This article explores how AI technologies can complement and enhance TCIM, aligning with the shared objectives of researchers from both fields in improving patient outcomes, enhancing care quality, and promoting holistic wellness. This integration of TCIM and AI introduces exciting opportunities but also noteworthy challenges. AI may augment TCIM by assisting in early disease detection, providing personalized treatment plans, predicting health trends, and enhancing patient engagement. Challenges at the intersection of AI and TCIM include data privacy and security, regulatory complexities, maintaining the human touch in patient-provider relationships, and mitigating bias in AI algorithms. Patients' trust, informed consent, and legal accountability are all essential considerations. Future directions in AI-enhanced TCIM include advanced personalized medicine, understanding the efficacy of herbal remedies, and studying patient-provider interactions. Research on bias mitigation, patient acceptance, and trust in AI-driven TCIM healthcare is crucial. In this article, we outlined that the merging of TCIM and AI holds great promise in enhancing healthcare delivery, personalizing treatment plans, preventive care, and patient engagement. Addressing challenges and fostering collaboration between AI experts, TCIM practitioners, and policymakers, however, is vital to harnessing the full potential of this integration.
Collapse
Affiliation(s)
- Jeremy Y. Ng
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Holger Cramer
- Institute of General Practice and Interprofessional Care, University Hospital Tübingen, Tübingen, Germany
- Robert Bosch Center for Integrative Medicine and Health, Bosch Health Campus, Stuttgart, Germany
| | - Myeong Soo Lee
- KM Science Research Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - David Moher
- Centre for Journalology, Ottawa Hospital Research Institute, Ottawa, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada
| |
Collapse
|
9
|
Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare (Basel) 2024; 12:481. [PMID: 38391856 PMCID: PMC10887513 DOI: 10.3390/healthcare12040481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024] Open
Abstract
Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.
Collapse
Affiliation(s)
- Dhir Gala
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Haditya Behl
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Mili Shah
- Department of Clinical Science, American University of the Caribbean School of Medicine, Cupecoy, Sint Maarten, The Netherlands
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hofstra University, 500 Hofstra Blvd., Hempstead, NY 11549, USA
- Department of Cardiology, Nassau University Medical Center, Hempstead, NY 11554, USA
| |
Collapse
|
10
|
Wu CC, Islam MM, Poly TN, Weng YC. Artificial Intelligence in Kidney Disease: A Comprehensive Study and Directions for Future Research. Diagnostics (Basel) 2024; 14:397. [PMID: 38396436 PMCID: PMC10887584 DOI: 10.3390/diagnostics14040397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
Collapse
Affiliation(s)
- Chieh-Chen Wu
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| | - Md. Mohaimenul Islam
- Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA;
| | - Tahmina Nasrin Poly
- Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan;
| | - Yung-Ching Weng
- Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan;
| |
Collapse
|
11
|
Vrudhula A, Kwan AC, Ouyang D, Cheng S. Machine Learning and Bias in Medical Imaging: Opportunities and Challenges. Circ Cardiovasc Imaging 2024; 17:e015495. [PMID: 38377237 PMCID: PMC10883605 DOI: 10.1161/circimaging.123.015495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Bias in health care has been well documented and results in disparate and worsened outcomes for at-risk groups. Medical imaging plays a critical role in facilitating patient diagnoses but involves multiple sources of bias including factors related to access to imaging modalities, acquisition of images, and assessment (ie, interpretation) of imaging data. Machine learning (ML) applied to diagnostic imaging has demonstrated the potential to improve the quality of imaging-based diagnosis and the precision of measuring imaging-based traits. Algorithms can leverage subtle information not visible to the human eye to detect underdiagnosed conditions or derive new disease phenotypes by linking imaging features with clinical outcomes, all while mitigating cognitive bias in interpretation. Importantly, however, the application of ML to diagnostic imaging has the potential to either reduce or propagate bias. Understanding the potential gain as well as the potential risks requires an understanding of how and what ML models learn. Common risks of propagating bias can arise from unbalanced training, suboptimal architecture design or selection, and uneven application of models. Notwithstanding these risks, ML may yet be applied to improve gain from imaging across all 3A's (access, acquisition, and assessment) for all patients. In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging, how ML may improve current approaches to imaging, and what specific design considerations should be made as part of efforts to maximize the quality of health care for all.
Collapse
Affiliation(s)
- Amey Vrudhula
- Icahn School of Medicine at Mount Sinai, New York
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
- Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center
| |
Collapse
|
12
|
Dickinson H, Teltsch DY, Feifel J, Hunt P, Vallejo-Yagüe E, Virkud AV, Muylle KM, Ochi T, Donneyong M, Zabinski J, Strauss VY, Hincapie-Castillo JM. The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness. Drug Saf 2024; 47:117-123. [PMID: 38019365 DOI: 10.1007/s40264-023-01376-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 11/30/2023]
Abstract
The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the 'safest' medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the 'true impact' that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.
Collapse
Affiliation(s)
| | | | - Jan Feifel
- Merck Healthcare KGaA, Darmstadt, Germany
| | - Philip Hunt
- Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, Switzerland
| | - Enriqueta Vallejo-Yagüe
- AstraZeneca, Gaithersberg, MD, USA
- Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland
| | - Arti V Virkud
- Kidney Center School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Taichi Ochi
- Department of PharmacoTherapy, Epidemiology and Economics, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, The Netherlands
- Center for Innovation in Medicine, Bucharest, Romania
| | | | | | - Victoria Y Strauss
- Boehringer Ingelheim, Binger Str. 173, 55218, Ingelheim am Rhein, Germany
| | | |
Collapse
|
13
|
Rider NL, Truxton A, Ohrt T, Margolin-Katz I, Horan M, Shin H, Davila R, Tenembaum V, Quinn J, Modell V, Modell F, Orange JS, Branner A, Senerchia C. Validating inborn error of immunity prevalence and risk with nationally representative electronic health record data. J Allergy Clin Immunol 2024:S0091-6749(24)00078-2. [PMID: 38278184 DOI: 10.1016/j.jaci.2024.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024]
Abstract
BACKGROUND The 10 Warning Signs of Primary Immunodeficiency were created 30 years ago to advance recognition of inborn errors of immunity (IEI). However, no population-level assessment of their utility applied to electronic health record (EHR) data has been conducted. OBJECTIVE We sought to quantify the value of having ≥2 warning signs (WS) toward diagnosing IEI using a highly representative real-world US cohort. A secondary goal was estimating the US prevalence of IEI. METHODS In this cohort study, we accessed normalized and de-identified EHR data on 152 million US patients. An IEI cohort (n = 41,080), in which patients were defined by having at least 1 verifiable IEI diagnosis placed ≥2 times in their record, was compared with a matched set of controls (n = 250,262). WS were encoded along with relevant diagnoses, relative weights were calculated, and the proportion of IEI cases versus controls with ≥2 WS was compared. RESULTS The proportion of IEI cases with ≥2 WS significantly differed from controls (0.33 vs 0.031; P < .0005, χ2 test). We also estimated a US IEI prevalence of 6 per 10,000 individuals (41,080/73,165,655; 0.056%). WS 9 (≥2 deep-seated infections), 7 (fungal infections), 5 (failure to thrive) and 4 (≥2 pneumonias in 1 year) were the most heavily weighted among the IEI cohort. CONCLUSIONS This nationally representative US-based cohort study demonstrates that presence of WS and associated clinical diagnoses can facilitate identification of patients with IEI from EHR data. In addition, we estimate that 6 in 10,000, or approximately 150,000 to 200,000 individuals are affected by IEI across the United States.
Collapse
Affiliation(s)
- Nicholas L Rider
- Department of Health Systems and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, Va.
| | - Ahuva Truxton
- Optum Clinical Trial Solutions, Optum Life Sciences, Eden Prairie, Minn
| | - Tracy Ohrt
- Optum Clinical Trial Solutions, Optum Life Sciences, Eden Prairie, Minn
| | | | - Mary Horan
- Optum Clinical Trial Solutions, Optum Life Sciences, Eden Prairie, Minn
| | - Harold Shin
- Division of Clinical Informatics, Liberty University College of Osteopathic Medicine, Lynchburg, Va
| | | | | | | | | | | | - Jordan S Orange
- Department of Pediatrics, Columbia University Vagelos College of Physicians and Surgeons, New York, NY
| | - Almut Branner
- Optum Clinical Trial Solutions, Optum Life Sciences, Eden Prairie, Minn
| | - Cynthia Senerchia
- Optum Clinical Trial Solutions, Optum Life Sciences, Eden Prairie, Minn
| |
Collapse
|
14
|
Abdaljaleel M, Barakat M, Alsanafi M, Salim NA, Abazid H, Malaeb D, Mohammed AH, Hassan BAR, Wayyes AM, Farhan SS, Khatib SE, Rahal M, Sahban A, Abdelaziz DH, Mansour NO, AlZayer R, Khalil R, Fekih-Romdhane F, Hallit R, Hallit S, Sallam M. A multinational study on the factors influencing university students' attitudes and usage of ChatGPT. Sci Rep 2024; 14:1983. [PMID: 38263214 PMCID: PMC10806219 DOI: 10.1038/s41598-024-52549-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 01/19/2024] [Indexed: 01/25/2024] Open
Abstract
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
Collapse
Affiliation(s)
- Maram Abdaljaleel
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, 11942, Jordan
| | - Muna Barakat
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
| | - Mariam Alsanafi
- Department of Pharmacy Practice, Faculty of Pharmacy, Kuwait University, Kuwait City, Kuwait
- Department of Pharmaceutical Sciences, Public Authority for Applied Education and Training, College of Health Sciences, Safat, Kuwait
| | - Nesreen A Salim
- Prosthodontic Department, School of Dentistry, The University of Jordan, Amman, 11942, Jordan
- Prosthodontic Department, Jordan University Hospital, Amman, 11942, Jordan
| | - Husam Abazid
- Department of Clinical Pharmacy and Therapeutics, Faculty of Pharmacy, Applied Science Private University, Amman, 11931, Jordan
| | - Diana Malaeb
- College of Pharmacy, Gulf Medical University, P.O. Box 4184, Ajman, United Arab Emirates
| | - Ali Haider Mohammed
- School of Pharmacy, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor Darul Ehsan, Malaysia
| | | | | | - Sinan Subhi Farhan
- Department of Anesthesia, Al Rafidain University College, Baghdad, 10001, Iraq
| | - Sami El Khatib
- Department of Biomedical Sciences, School of Arts and Sciences, Lebanese International University, Bekaa, Lebanon
- Center for Applied Mathematics and Bioinformatics (CAMB), Gulf University for Science and Technology (GUST), 32093, Hawally, Kuwait
| | - Mohamad Rahal
- School of Pharmacy, Lebanese International University, Beirut, 961, Lebanon
| | - Ali Sahban
- School of Dentistry, The University of Jordan, Amman, 11942, Jordan
| | - Doaa H Abdelaziz
- Pharmacy Practice and Clinical Pharmacy Department, Faculty of Pharmacy, Future University in Egypt, Cairo, 11835, Egypt
- Department of Clinical Pharmacy, Faculty of Pharmacy, Al-Baha University, Al-Baha, Saudi Arabia
| | - Noha O Mansour
- Clinical Pharmacy and Pharmacy Practice Department, Faculty of Pharmacy, Mansoura University, Mansoura, 35516, Egypt
- Clinical Pharmacy and Pharmacy Practice Department, Faculty of Pharmacy, Mansoura National University, Dakahlia Governorate, 7723730, Egypt
| | - Reem AlZayer
- Clinical Pharmacy Practice, Department of Pharmacy, Mohammed Al-Mana College for Medical Sciences, 34222, Dammam, Saudi Arabia
| | - Roaa Khalil
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan
| | - Feten Fekih-Romdhane
- The Tunisian Center of Early Intervention in Psychosis, Department of Psychiatry "Ibn Omrane", Razi Hospital, 2010, Manouba, Tunisia
- Faculty of Medicine of Tunis, Tunis El Manar University, Tunis, Tunisia
| | - Rabih Hallit
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
- Department of Infectious Disease, Bellevue Medical Center, Mansourieh, Lebanon
- Department of Infectious Disease, Notre Dame des Secours, University Hospital Center, Byblos, Lebanon
| | - Souheil Hallit
- School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
- Research Department, Psychiatric Hospital of the Cross, Jal Eddib, Lebanon
| | - Malik Sallam
- Department of Pathology, Microbiology and Forensic Medicine, School of Medicine, The University of Jordan, Amman, 11942, Jordan.
- Department of Clinical Laboratories and Forensic Medicine, Jordan University Hospital, Amman, 11942, Jordan.
| |
Collapse
|
15
|
Miles RC, Porras AR. Implications of Data Representation in Health Care Innovation. Radiol Imaging Cancer 2024; 6:e230222. [PMID: 38276908 PMCID: PMC10825701 DOI: 10.1148/rycan.230222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 01/27/2024]
Affiliation(s)
- Randy C. Miles
- From the Department of Radiology, Denver Health, 777 Bannock St,
Denver, CO 80204 (R.C.M.); Department of Biostatistics and Informatics, Colorado
School of Public Health, University of Colorado Anschutz Medical Campus, Aurora,
Colo (A.R.P.)
| | - Antonio R. Porras
- From the Department of Radiology, Denver Health, 777 Bannock St,
Denver, CO 80204 (R.C.M.); Department of Biostatistics and Informatics, Colorado
School of Public Health, University of Colorado Anschutz Medical Campus, Aurora,
Colo (A.R.P.)
| |
Collapse
|
16
|
BaHammam AS. Balancing Innovation and Integrity: The Role of AI in Research and Scientific Writing. Nat Sci Sleep 2023; 15:1153-1156. [PMID: 38170140 PMCID: PMC10759812 DOI: 10.2147/nss.s455765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Affiliation(s)
- Ahmed S BaHammam
- Editor-in-Chief Nature and Science of Sleep
- Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, Riyadh, Saudi Arabia
- King Saud University Medical City, Riyadh, Saudi Arabia
| |
Collapse
|
17
|
Fereshtehnejad SM, Lökk J. Challenges of Teleneurology in the Care of Complex Neurodegenerative Disorders: The Case of Parkinson's Disease with Possible Solutions. Healthcare (Basel) 2023; 11:3187. [PMID: 38132077 PMCID: PMC10742857 DOI: 10.3390/healthcare11243187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
Teleneurology is a specialist field within the realm of telemedicine, which is dedicated to delivering neurological care and consultations through virtual encounters. Teleneurology has been successfully used in acute care (e.g., stroke) and outpatient evaluation for chronic neurological conditions such as epilepsy and headaches. However, for some neurologic entities like Parkinson's disease, in which an in-depth physical examination by palpating muscles and performing neurologic maneuvers is the mainstay of monitoring the effects of medication, the yield and feasibility of a virtual encounter are low. Therefore, in this prospective review, we discuss two promising teleneurology approaches and propose adjustments to enhance the value of virtual encounters by improving the validity of neurological examination: 'hybrid teleneurology', which involves revising the workflow of virtual encounters; and 'artificial intelligence (AI)-assisted teleneurology', namely the use of biosensors and wearables and data processing using AI.
Collapse
Affiliation(s)
- Seyed-Mohammad Fereshtehnejad
- Edmond J. Safra Program in Parkinsonߣs Disease and Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, UHN, Toronto, ON M5T 2S8, Canada
- Institute of Health Policy, Management and Evaluation (IHPME), Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A1, Canada
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, 171 77 Stockholm, Sweden;
| | - Johan Lökk
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, 171 77 Stockholm, Sweden;
| |
Collapse
|
18
|
Fraser HS, Marcelo A, Kalla M, Kalua K, Celi LA, Ziegler J. Digital determinants of health: Editorial. PLOS Digit Health 2023; 2:e0000373. [PMID: 38016101 PMCID: PMC10684281 DOI: 10.1371/journal.pdig.0000373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Affiliation(s)
- Hamish S. Fraser
- Brown Center for Biomedical Informatics, Brown University, Providence, Rhode Island, United States of America
| | - Alvin Marcelo
- Medical Informatics Unit, University of the Philippines, Manila, The Philippines
| | - Mahima Kalla
- Centre for Digital Transformation of Health, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Khumbo Kalua
- Blantyre Institute for Community Outreach and the College of Medicine, University of Malawi, Blantyre, Malawi
| | - Leo A. Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Jennifer Ziegler
- Department of Internal Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| |
Collapse
|
19
|
Glocker B, Jones C, Roschewitz M, Winzeck S. Risk of Bias in Chest Radiography Deep Learning Foundation Models. Radiol Artif Intell 2023; 5:e230060. [PMID: 38074789 PMCID: PMC10698597 DOI: 10.1148/ryai.230060] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 08/07/2023] [Accepted: 08/24/2023] [Indexed: 03/15/2024]
Abstract
PURPOSE To analyze a recently published chest radiography foundation model for the presence of biases that could lead to subgroup performance disparities across biologic sex and race. MATERIALS AND METHODS This Health Insurance Portability and Accountability Act-compliant retrospective study used 127 118 chest radiographs from 42 884 patients (mean age, 63 years ± 17 [SD]; 23 623 male, 19 261 female) from the CheXpert dataset that were collected between October 2002 and July 2017. To determine the presence of bias in features generated by a chest radiography foundation model and baseline deep learning model, dimensionality reduction methods together with two-sample Kolmogorov-Smirnov tests were used to detect distribution shifts across sex and race. A comprehensive disease detection performance analysis was then performed to associate any biases in the features to specific disparities in classification performance across patient subgroups. RESULTS Ten of 12 pairwise comparisons across biologic sex and race showed statistically significant differences in the studied foundation model, compared with four significant tests in the baseline model. Significant differences were found between male and female (P < .001) and Asian and Black (P < .001) patients in the feature projections that primarily capture disease. Compared with average model performance across all subgroups, classification performance on the "no finding" label decreased between 6.8% and 7.8% for female patients, and performance in detecting "pleural effusion" decreased between 10.7% and 11.6% for Black patients. CONCLUSION The studied chest radiography foundation model demonstrated racial and sex-related bias, which led to disparate performance across patient subgroups; thus, this model may be unsafe for clinical applications.Keywords: Conventional Radiography, Computer Application-Detection/Diagnosis, Chest Radiography, Bias, Foundation Models Supplemental material is available for this article. Published under a CC BY 4.0 license.See also commentary by Czum and Parr in this issue.
Collapse
Affiliation(s)
- Ben Glocker
- From the Department of Computing, Imperial College London, South
Kensington Campus, London SW7 2AZ, United Kingdom
| | - Charles Jones
- From the Department of Computing, Imperial College London, South
Kensington Campus, London SW7 2AZ, United Kingdom
| | - Mélanie Roschewitz
- From the Department of Computing, Imperial College London, South
Kensington Campus, London SW7 2AZ, United Kingdom
| | - Stefan Winzeck
- From the Department of Computing, Imperial College London, South
Kensington Campus, London SW7 2AZ, United Kingdom
| |
Collapse
|
20
|
Teotia K, Jia Y, Woite NL, Celi LA, Matos J, Struja T. Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations. medRxiv 2023:2023.10.12.23296568. [PMID: 37873163 PMCID: PMC10593024 DOI: 10.1101/2023.10.12.23296568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Objective Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). Methods Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. Results We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. Conclusion We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.
Collapse
|
21
|
Holmes Fee C, Hicklen RS, Jean S, Abu Hussein N, Moukheiber L, de Lota MF, Moukheiber M, Moukheiber D, Anthony Celi L, Dankwa-Mullan I. Strategies and solutions to address Digital Determinants of Health (DDOH) across underinvested communities. PLOS Digit Health 2023; 2:e0000314. [PMID: 37824481 PMCID: PMC10569606 DOI: 10.1371/journal.pdig.0000314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
Healthcare has long struggled to improve services through technology without further widening health disparities. With the significant expansion of digital health, a group of healthcare professionals and scholars from across the globe are proposing the official usage of the term "Digital Determinants of Health" (DDOH) to explicitly call out the relationship between technology, healthcare, and equity. This is the final paper in a series published in PLOS Digital Health that seeks to understand and summarize current knowledge of the strategies and solutions that help to mitigate the negative effects of DDOH for underinvested communities. Through a search of English-language Medline, Scopus, and Google Scholar articles published since 2010, 345 articles were identified that discussed the application of digital health technology among underinvested communities. A group of 8 reviewers assessed 132 articles selected at random for the mention of solutions that minimize differences in DDOH. Solutions were then organized by categories of policy; design and development; implementation and adoption; and evaluation and ongoing monitoring. The data were then assessed by category and the findings summarized. The reviewers also looked for common themes across the solutions and evidence of effectiveness. From this limited scoping review, the authors found numerous solutions mentioned across the papers for addressing DDOH and many common themes emerged regardless of the specific community or digital health technology under review. There was notably less information on solutions regarding ongoing evaluation and monitoring which corresponded with a lack of research evidence regarding effectiveness. The findings directionally suggest that universal strategies and solutions can be developed to address DDOH independent of the specific community under focus. With the need for the further development of DDOH measures, we also provide a framework for DDOH assessment.
Collapse
Affiliation(s)
- Casey Holmes Fee
- Healthcare Consultant, Newton, Massachusetts, United States of America
| | - Rachel Scarlett Hicklen
- Research Medical Library, MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Sidney Jean
- Massachusetts Executive Office of Health and Human Services, Boston, Massachusetts, United States of America
- Simmons University, Boston, Massachusetts, United States of America
| | - Nebal Abu Hussein
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
- Department for BioMedical Research DBMR, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lama Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | | | - Mira Moukheiber
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Dana Moukheiber
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Leo Anthony Celi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Pain Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Irene Dankwa-Mullan
- Marti Health, Atlanta, Georgia, United States of America
- Department of Health Policy and Management, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States of America
| |
Collapse
|