Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00208-9 · Cited by 1,724

Automation bias: a systematic review of frequency, effect mediators, and mitigators

Goddard, K., Roudsari, A., & Wyatt, J. C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000089 · Cited by 1,162

Explainability and artificial intelligence in medicine

Reddy, S. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00029-2 · Cited by 246

Machine learning in precision diabetes care and cardiovascular risk prediction

Oikonomou, E. K., & Khera, R. (2023) · Cardiovascular Diabetology · doi:10.1186/s12933-023-01985-3 · Cited by 190

Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

Sangha, V., Nargesi, A. A., Dhingra, L. S., Khunte, A., Mortazavi, B. J., Ribeiro, A. H., Banina, E., Adeola, O., Garg, N., Brandt, C. A., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.062646 · Cited by 150

Severe aortic stenosis detection by deep learning applied to echocardiography

Holste, G., Oikonomou, E. K., Mortazavi, B. J., Coppi, A. C., Faridi, K. F., Miller, E. J., Forrest, J. K., McNamara, R. L., Lucila Ohno-machado, A., Yuan, N., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad456 · Cited by 149

Approaching autonomy in medical artificial intelligence

Bitterman, D. S., Aerts, H. J. W. L., & Mak, R. H. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30187-4 · Cited by 107

Characterizing the clinical adoption of medical AI devices through U.S. insurance claims

Wu, K., Wu, E., Theodorou, B., Liang, W., Mack, C., Glass, L., Sun, J., & Zou, J. (2024) · NEJM AI · doi:10.1056/aioa2300030 · Cited by 103

AI in Medicine—JAMA’s Focus on Clinical Outcomes, Patient-Centered Care, Quality, and Equity

Khera, R., Butte, A. J., Berkwits, M., Hswen, Y., Flanagin, A., Park, H., Curfman, G., & Bibbins-Domingo, K. (2023) · JAMA · doi:10.1001/jama.2023.15481 · Cited by 78

Prevention of Bias and Discrimination in Clinical Practice Algorithms

Shachar, C., & Gerke, S. (2023) · JAMA · doi:10.1001/jama.2022.23867 · Cited by 53

AI, Health, and Health Care Today and Tomorrow: The JAMA Summit Report on Artificial Intelligence

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., et al. (2025) · JAMA · doi:10.1001/jama.2025.18490 · Cited by 195

Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects

Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168

The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges

Bulut, O., & Beiting-Parrish, M. (2024) · Chinese/English Journal of Educational Measurement and Evaluation · doi:10.59863/miql7785 · Cited by 272

Oculomics: Current concepts and evidence

Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101350 · Cited by 97

Reporting Use of AI in Research and Scholarly Publication—JAMA Network Guidance

Flanagin, A., Pirracchio, R., Khera, R., Berkwits, M., Hswen, Y., & Bibbins-Domingo, K. B. (2024) · JAMA · doi:10.1001/jama.2024.3471 · Cited by 100

Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association

Khera, R., Asnani, A. H., Krive, J., Addison, D., Zhu, H., Vasbinder, A. L., Fleming, M. R., Arnaout, R., Razavi, P., & Okwuosa, T. M. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/hcg.0000000000000097 · Cited by 83

DeepSeek’s “Low-Cost” Adoption Across China’s Hospital Systems

Zeng, D., Qin, Y., Sheng, B., & Wong, T. Y. (2025) · JAMA · doi:10.1001/jama.2025.6571 · Cited by 73

What Is the Role of Explainability in Medical Artificial Intelligence? A Case-Based Approach

Hildt, E. (2025) · Bioengineering · doi:10.3390/bioengineering12040375 · Cited by 74

Human–AI collectives most accurately diagnose clinical vignettes

Zöller, N., Berger, J., Lin, I., Fu, N., Komarneni, J., Barabucci, G., Laskowski, K., Shia, V., Harack, B., Chu, E. A., et al. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2426153122 · Cited by 74

Current status and future directions of explainable artificial intelligence in medical imaging

Saw, S. N., Yan, Y. Y., & Ng, K. H. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111884 · Cited by 64

Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models

Bouguettaya, A., Stuart, E. M., & Aboujaoude, E. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01746-4 · Cited by 60

Artificial intelligence-enhanced echocardiography in cardiovascular disease management

Myhre, P. L., Grenne, B. L., Asch, F. M., Delgado, V., Khera, R., Lafitte, S., Lang, R. M., Pellikka, P. A., Sengupta, P. P., Vemulapalli, S., et al. (2025) · Nature Reviews Cardiology · doi:10.1038/s41569-025-01197-0 · Cited by 59

A systematic review of clinicians’ acceptance and use of clinical decision support systems over time

Newton, N., Bamgboje‐Ayodele, A., Forsyth, R., Tariq, A., & Baysari, M. T. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01662-7 · Cited by 49

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart Association

Khan, S. S., Greenland, P., Hayman, L. L., Khera, R., Návar, A. M., Pencina, M. J., Reza, N., Shah, S. H., Shanbhag, S. M., Weber, B., et al. (2026) · Circulation · doi:10.1161/cir.0000000000001401 · Cited by 45

Transforming diagnosis through artificial intelligence

D’Adderio, L., & Bates, D. W. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01460-1 · Cited by 35

Artificial intelligence-enhanced patient evaluation: bridging art and science

Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae415 · Cited by 34

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography

Kim, S. H., Schramm, S., Riedel, E. O., Schmitzer, L., Rosenkranz, E., Kertels, O., Bodden, J. H., Paprottka, K. J., Sepp, D., Renz, M., et al. (2025) · La radiologia medica · doi:10.1007/s11547-025-01964-6 · Cited by 36

Ambient Listening—Legal and Ethical Issues

Cohen, I. G., Ritzman, J., & Cahill, R. F. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.60642 · Cited by 32

Facilitating Trust Calibration in Artificial Intelligence–Driven Diagnostic Decision Support Systems for Determining Physicians’ Diagnostic Accuracy: Quasi-Experimental Study

Sakamoto, T., Harada, Y., & Shimizu, T. (2024) · JMIR Formative Research · doi:10.2196/58666 · Cited by 36

Minimizing bias when using artificial intelligence in critical care medicine

Ranard, B. L., Park, S., Jia, Y., Zhang, Y., Alwan, F. S., Celi, L. A., & Lusczek, E. R. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154796 · Cited by 27

Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility

Oikonomou, E. K., & Khera, R. (2024) · Hellenic Journal of Cardiology · doi:10.1016/j.hjc.2024.07.003 · Cited by 26

Artificial intelligence in medicine: a scoping review of the risk of deskilling and loss of expertise among physicians

Heudel, P., Crochet, H., Filori, Q., Bachelot, T., & Blay, J. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100693 · Cited by 33

Calibrating AI Reliance—A Physician’s Superhuman Dilemma

Patil, S. V., Myers, C. G., & Lu‐Myers, Y. (2025) · JAMA Health Forum · doi:10.1001/jamahealthforum.2025.0106 · Cited by 26

Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support

Weissman, G. E. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095824 · Cited by 31

Empirically derived evaluation requirements for responsible deployments of AI in safety-critical settings

Morey, D. A., Rayo, M. F., & Woods, D. D. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01784-y · Cited by 28

Pragmatic Approaches to the Evaluation and Monitoring of Artificial Intelligence in Health Care: A Science Advisory From the American Heart Association

Jain, S. S., Goto, S., Hall, J. L., Khan, S. S., MacRae, C. A., Ofori, C., Pegus, C., Pencina, M., Peterson, E. D., Schwamm, L. H., et al. (2025) · Circulation · doi:10.1161/cir.0000000000001400 · Cited by 23

Crucial Role of Understanding in Human-Artificial Intelligence Interaction for Successful Clinical Adoption

Park, S. H., & Langlotz, C. P. (2025) · Korean Journal of Radiology · doi:10.3348/kjr.2025.0071 · Cited by 22

Meaningful oversight of medical AI beyond human in the loop

van de Sande, D., Economou-Zavlanos, N., & van Genderen, M. E. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02971-1 · Cited by 29

Potential risks of GenAI on medical education

Hough, J., Culley, N., Erganian, C., & Alahdab, F. (2025) · BMJ Evidence-Based Medicine · doi:10.1136/bmjebm-2025-114339 · Cited by 19

ChatGPT Yields a Passing Score on a Pediatric Board Preparatory Exam but Raises Red Flags

Le, M., & Davis, M. K. (2024) · Global Pediatric Health · doi:10.1177/2333794x241240327 · Cited by 21

Easing the cognitive load of general practitioners: AI design principles for future-ready healthcare

Hor, T., Fong, L., Wynne, K., & Verhoeven, B. (2025) · Technovation · doi:10.1016/j.technovation.2025.103208 · Cited by 17

An automated information extraction model for unstructured discharge letters using large language models and GPT-4

Siepmann, R. M., Baldini, G., Schmidt, C. S., Truhn, D., Müller‐Franzes, G. A., Dada, A., Kleesiek, J., Nensa, F., & Hosch, R. (2025) · Healthcare Analytics · doi:10.1016/j.health.2024.100378 · Cited by 17

Preparing for the Widespread Adoption of Clinic Visit Recording

Barr, P. J., Gramling, R. E., & Vosoughi, S. (2024) · NEJM AI · doi:10.1056/aip2400392 · Cited by 15

Artificial Intelligence for Cardiovascular Care in Action

Biswas, D., Thangaraj, P., Pedroso, A. F., Ogunniyi, M. O., Maddox, T. M., Ahmad, F. S., & Khera, R. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.102307 · Cited by 17

Artificial intelligence-enhanced exposomics: novel insights into cardiovascular health

Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae159 · Cited by 14

Eye Tracking-Enhanced Deep Learning for Medical Image Analysis: A Systematic Review on Data Efficiency, Interpretability, and Multimodal Integration

Duan, J., Zhang, M., Song, M., Xu, X., & Lu, H. (2025) · Bioengineering · doi:10.3390/bioengineering12090954 · Cited by 13

Comprehensive Management of Intracranial Aneurysms Using Artificial Intelligence: An Overview

Xue, J., Zheng, H., Lai, R., Zhou, Z., Zhou, J., Chen, L., & Wang, M. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.10.108 · Cited by 13

Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data

Pedroso, A. F., Schwamm, L. H., & Khera, R. (2025) · Stroke · doi:10.1161/strokeaha.125.050447 · Cited by 15

The Future of Allergy Management: How Artificial Intelligence Is Changing the Game

Cilluffo, G., Bajowala, S. S., Elliott, T., Gupta, P., Volertas, S., & Ferrante, G. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.08.033 · Cited by 11

Malpractice in the machine age: Legal and ethical responses to machine learning in medical imaging

Chau, M., Spuur, K. M., White, S. R., Pyper, A., & Crossman, M. H. (2026) · Radiography · doi:10.1016/j.radi.2026.103339 · Cited by 11

Prospective Clinical Implementation of Paige Prostate Detect Artificial Intelligence Assistance in the Detection of Prostate Cancer in Prostate Biopsies: CONFIDENT P Trial Implementation of Artificial Intelligence Assistance in Prostate Cancer Detection

Flach, R. N., van Dooijeweert, C., Nguyen, T. Q., Lynch, M., Jonges, T. G. N., Meijer, R. P., Suelmann, B. B. M., Willemse, P.-P. M., Stathonikos, N., & van Diest, P. J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-24-00193 · Cited by 11

Evaluating Explainability: A Framework for Systematic Assessment of Explainable AI Features in Medical Imaging

Lago, M. A., Zamzmi, G., Eich, B., & Delfino, J. G. (2026) · Bioengineering · doi:10.3390/bioengineering13010111 · Cited by 12

Transforming Physiology and Healthcare through Foundation Models

Godwin, R. C., Tung, A., Berkowitz, D. E., & Melvin, R. L. (2025) · Physiology · doi:10.1152/physiol.00048.2024 · Cited by 11

AI-driven report-generation tools in mental healthcare: A review of commercial tools

Bouguettaya, A., Victoria Team, Stuart, E. M., & Aboujaoude, E. N. (2025) · General Hospital Psychiatry · doi:10.1016/j.genhosppsych.2025.02.018 · Cited by 11

Evaluating the o1 reasoning large language model for cognitive bias: a vignette study

Degany, O., Laros, S., Idan, D., & Einav, S. (2025) · Critical Care · doi:10.1186/s13054-025-05591-5 · Cited by 10

Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)

Fang, Z., Johnston, A., Cheuy, L. Y., Na, H. S., Paschali, M., González, C., Armstrong, B. A., Koirala, A., Laurel, D., Campion, A., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02007-0 · Cited by 11

Human-centered design of an artificial intelligence monitoring system: the Vanderbilt Algorithmovigilance Monitoring and Operations System

Salwei, M. E., Davis, S. E., Reale, C., Novak, L. L., Walsh, C. G., Beebe, R., Nelson, S. D., Sundrani, S. C., Rose, S. L., Wright, A. T., et al. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf136 · Cited by 13

Understanding and Addressing Bias in Artificial Intelligence Systems: A Primer for the Emergency Medicine Physician

Abbott, E. E., Rehman, T., Rosania, A. R., Lum, D. L., Taylor, T. B., Kirk, A., Taylor, R. A., Baker, E. F., Rabin, E., Padela, A. I., et al. (2025) · Journal of the American College of Emergency Physicians Open · doi:10.1016/j.acepjo.2025.100311 · Cited by 15

Governing Generative AI in Healthcare: A Normative Conceptual Framework for Epistemic Authority, Trust, and the Architecture of Responsibility

Akgün, F. E., & Akgün, M. (2026) · Healthcare · doi:10.3390/healthcare14081098 · Cited by 13

Artificial intelligence and machine learning in hemostasis and thrombosis

Gresele, P. (2023) · Bleeding Thrombosis and Vascular Biology · doi:10.4081/btvb.2023.105 · Cited by 10

Reasons in the Loop: The Role of Large Language Models in Medical Co-Reasoning

Mann, S. P., Earp, B. D., Liu, P., & Savulescu, J. (2024) · The American Journal of Bioethics · doi:10.1080/15265161.2024.2383121 · Cited by 9

Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care

Aminorroaya, A., Biswas, D., Pedroso, A. F., & Khera, R. (2025) · Journal of the Society for Cardiovascular Angiography & Interventions · doi:10.1016/j.jscai.2025.102562 · Cited by 10

Equality, diversity, and inclusion in artificial intelligence-driven healthcare chatbots: addressing challenges and shaping strategies

Küçükkaya, A., Aktaş, E., Moons, P., & Göktaş, P. (2025) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvaf104 · Cited by 9

Barriers and Facilitators to the Preadoption of a Computer-Aided Diagnosis Tool for Cervical Cancer: Qualitative Study on Health Care Providers’ Perspectives in Western Cameroon

Cattin, M., Datchoua, A. M., Yakam, V. F., Kenfack, B., Petignat, P., Thiran, J., Schönenberger, K., & Schmidt, N. C. (2024) · JMIR Cancer · doi:10.2196/50124 · Cited by 7

Harmful epistemic dependence on medical machine learning and its moral implications

Pozzi, G., Buijsman, S., & van den Hoven, J. (2025) · Journal of Medical Ethics · doi:10.1136/jme-2024-110552 · Cited by 7

Amplifying Chinese physicians’ emphasis on patients’ psychological states beyond urologic diagnoses with ChatGPT – a multicenter cross-sectional study

Lei, P., Liang, R., Zhao, A., Sun, R., Yi, F., Zhong, J., Li, R., Zhu, S., Zhang, S., & Wu, S. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001775 · Cited by 7

The algorithmic consultant: a new era of clinical AI calls for a new workforce of physician-algorithm specialists

Marwaha, J. S., Yuan, W., Poddar, M., Elsamadisi, P., & Brat, G. A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01960-0 · Cited by 11

Applications, image analysis, and interpretation of computer vision in medical imaging

Matsuzaka, Y., & Iyoda, M. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2025.1733003 · Cited by 12

The cognitive ecology of medicine: generative AI, clinical reasoning, and the preservation of adaptive expertise

Chow, M., Li, A., Quek, D. Y. J., Fan, E. B., & Sadasiv, M. S. (2026) · Postgraduate Medical Journal · doi:10.1093/postmj/qgag068 · Cited by 6

Generative pre-trained transformer reinforces historical gender bias in diagnosing women’s cardiovascular symptoms

Krieger, K., Hameed, I., Quer, G., Mack, C. A., Savic, M., Mantaj, P., Hirofuji, A., Gregg, A. C., Soletti, G. J., Rossi, C. S., et al. (2025) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztaf131 · Cited by 5

Artificial intelligence for cardiology: from diagnosis to management

Naidoo, V., Madamshetty, L., & Krishna, S. B. N. (2026) · Therapeutic Advances in Cardiovascular Disease · doi:10.1177/17539447251406847 · Cited by 5

How Generative AI Should Transform Clinical Decision Support

Liu, S. H., Peterson, J. F., & Wright, A. (2026) · JAMA · doi:10.1001/jama.2026.13187 · Cited by 7

Real-world human-LLM Interactions-Prospective blinded versus unblinded expert physician assessments of LLM responses to complex medical dilemmas

Ben Shitrit, I., Idan, D., Volevich, M., Goldenberg, H. S., Vaknin, D., Degany, O., Abelson, N., Binyamin, Y., Nassar, R., Nassar, M., et al. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001278 · Cited by 2

Empathy is More Than Just a Word: Reflecting on “Empathic” Chatbots and the Purpose of Using Generative AI in Healthcare

Guersenzvaig, A., & Delgado, J. (2025) · Nursing and Health Sciences · doi:10.1111/nhs.70233 · Cited by 4

Updated critical quality criteria for real-world artificial intelligence implementation

Oikonomou, E. K., Asselbergs, F. W., & Vardas, P. E. (2026) · European Heart Journal · doi:10.1093/eurheartj/ehag121 · Cited by 4

A systematic audit of transparency and validation disclosure in commercial veterinary artificial intelligence

Brundage, D. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1761038 · Cited by 4

Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care

Xie, S. J., Spice, C., Wedgeworth, P., Langevin, R., Lybarger, K., Singh, A., Wood, B. R., Klein, J. W., Hsieh, G., Duber, H. C., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf046 · Cited by 5

Proceedings of the world summit on parkinson’s disease

Mantri, S., Ghilardi, M. F., Lessard, N., Norcini, M., Rocco, A. D., Wallock, K., Lehr, J. L., & Okun, M. S. (2025) · npj Parkinson s Disease · doi:10.1038/s41531-025-01123-8 · Cited by 4

Interviews with clinicians about an ambient artificial intelligence documentation platform

Stults, C. D., Martinez, M., Szwerinski, N., Rabbani, N., & Jones, V. G. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag021 · Cited by 5

Bridging the Implementation Gap in Medical AI Education: 3-Lever Framework for Concurrent Reform

Kelly, S. L., Forgie, M., & Walker, M. C. (2026) · JMIR Medical Education · doi:10.2196/88778 · Cited by 5

LiverAI: New tool in the landscape for liver health

Aguado, D. M., Pazó, J., Díaz‐González, Á., de la Cadena, B. D. L. H. P., Conthe, A., Durán, R. G., Rodríguez-Gandía, M. Á., Turnés, J., & Romero‐Gómez, M. (2024) · Gastroenterología y Hepatología · doi:10.1016/j.gastrohep.2024.04.001 · Cited by 4

Artificial intelligence in personalized prescription: A narrative review of promise, peril, and practicality

Martin, G. L., & Létinier, L. (2025) · Therapies · doi:10.1016/j.therap.2025.10.001 · Cited by 3

Current and future use of artificial intelligence in valvular heart disease imaging

Sengupta, P. P., Poterucha, T. J., Pezel, T., Tsang, T. S. M., & Cosyns, B. (2025) · European Heart Journal - Cardiovascular Imaging · doi:10.1093/ehjci/jeaf348 · Cited by 3

Early prediction of vasopressor initiation in ICU sepsis patients using an interpretable EHR-based ML model

Duval, L., Villié, A., Zheng, F., Terraz, G., Blein, S., Duperchy, E., Everett, M. J., Frieling, J. T. M., Llitjos, J., & Bodinier, M. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03274-3 · Cited by 3

Enhancing Physician Resilience to Generative AI: Multilevel Framework for Shared Authority, Verification, and Skill Preservation

Pan, H., Liu, J., & Liu, S. (2026) · Journal of Medical Internet Research · doi:10.2196/88058 · Cited by 5

Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework

Wójcik, S., Rulkiewicz, A., & Domienik‐Karłowicz, J. (2026) · Healthcare · doi:10.3390/healthcare14162519 · Cited by 3

The Holy Grail of obstetric ultrasound: can artificial intelligence detect hard‐to‐identify fetal cardiac anomalies?

Drukker, L. (2024) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.27703 · Cited by 3

Evolving Landscape of Ethics in Oncology: A Journey Through the Past, Present, and Future

Nortjé, N., Palmer, A., Enck, G., Masciari, C. F., Neumann, J. L., & Gallagher, C. M. (2024) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_100043 · Cited by 3

Simulating Vulnerability: An Examination of Ethical, Legal and Social Aspects in the Context of Training Child Protection Workers

Michael, R., Kutza, J.-O., Werth, P., Liebe, J.-D., & Schöning, J. (2025) · IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS) · doi:10.1109/ethics65148.2025.11098302 · Cited by 2

Stakeholder Perspectives on Humanistic Implementation of Computer Perception in Health Care: Qualitative Study

Kostick, K. M., Hurley, M. E., Ayaz, S., Herrington, J. D., Zampella, C., Parish‐Morris, J., Tunç, B., Lázaro‐Muñoz, G., Blumenthal‐Barby, J., & Storch, E. A. (2025) · JMIR Mental Health · doi:10.2196/79182 · Cited by 3

Evaluating sociodemographic bias in a deployed machine-learned patient deterioration model

Colacci, M., Pou-Prom, C., Siddiqi, A., Mamdani, M., & Verma, A. A. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf158 · Cited by 2

The Role of Artificial Intelligence in Workforce Adaptation and Skill Development

Karmali, Y. (2026) · Biomedical visualization · doi:10.1007/978-3-032-06520-9_10 · Cited by 2

Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance

Brown, K. E., Wrenn, J. O., Jackson, N. J., Cauley, M. R., Collins, B. X., Novak, L. L., Malin, B., & Ancker, J. S. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf235 · Cited by 2

AI-assisted fibrosis scoring in MASH: Exploring pathologist decision-making with an SHG-based AI digital pathology tool

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