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

TRIAGE: Trustworthy Reporting and Assessment for Clinical Gain and Effectiveness of AI Models

Fazilati, F., Rajabi, M. T., Alihosseini, N., Farsani, M. E., Sandid, S. H., Zamani, S., Farahani, M. A., Biriaei, F., Sadeghipour, F., Mirshamsi, M. T., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16050666 · Cited by 5

Advanced Technologies in Medicine: Safeguarding Physician Skill, Knowledge, and Character in the AI-Enhanced Clinic

Palmer, A., & Schwan, D. (2025) · The International Library of Bioethics · doi:10.1007/978-3-031-94690-5_4 · Cited by 1

Public hesitancy for AI-based detection of neurodegenerative diseases in France

Rafaï, I., Davin-Casalena, B., Dubois, D., Blayac, T., & Ventelou, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-11917-8 · Cited by 3

Review and Commentary on Digital Pathology and Artificial Intelligence in Pathology

Sirintrapun, S. J. (2025) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00017 · Cited by 1

A Dermatologist’s Beginner’s Guide: Using Ambient Artificial Intelligence in Your Practice

Thakur, J. (2025) · Canadian dermatology today. · doi:10.58931/cdt.2025.63147 · Cited by 1

Automating cancer registries: Pearls and pitfalls

Satheakeerthy, S., Beecher, M., Booth, A. E. C., Stretton, B., Kovoor, J. J., Gupta, A. K., Evans, S., Howson, S. A., Logan, J., Qian, C. L., et al. (2025) · Health Information Management Journal · doi:10.1177/18333583251377892 · Cited by 1

AI in Diagnostic Radiology: What Happens When Algorithms Are Updated

Skregelid, M. R., Ibrahim-Pur, K., Skjøth, F., Pedersen, M. R. V., & Precht, H. (2026) · Radiation · doi:10.3390/radiation6010004 · Cited by 1

Institutionalizing trust in AI governance: from ethical principles to legal design

Lyu, X. (2026) · AI and Ethics · doi:10.1007/s43681-026-01009-2 · Cited by 1

Advances in Spinal Surgery: 3D Printing, Augmented Reality and Artificial Intelligence

Yahanda, A. T., Krishnan, A., Joseph, K., & Molina, C. A. (2026) · Springer eBooks · doi:10.1007/978-981-96-7448-0_38-2 · Cited by 1

Artificial Intelligence-enhanced Electrocardiography for Heart Failure Screening and Risk Stratification

Dhingra, L. S., Croon, P. M., Batinica, B., Aminorroaya, A., Pedroso, A. F., & Khera, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00748-x · Cited by 3

Artificial intelligence in emergency medicine critical care

Heslin, S. M., Nocito, R., & Weingart, S. D. (2026) · Clinical and Experimental Emergency Medicine · doi:10.15441/ceem.26.057 · Cited by 1

Digital upskilling of the health workforce – A university perspective

Cheong, B. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44073-1.00032-8 · Cited by 1

Ethical Considerations for Artificial Intelligence Tools in Small Animal Veterinary Medicine

Coghlan, S., & Quinn, T. (2026) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2026.03.001 · Cited by 1

Digital bioethics: exploring an emerging field

Starke, G., Balke, W., Benzinger, L., Buchholz, O., Burghardt, M., Meier, L. J., Mihailov, E., Seidlmayer, E., Ranisch, R., Ursin, F., et al. (2026) · Medicine Health Care and Philosophy · doi:10.1007/s11019-026-10347-1 · Cited by 1

Artificial intelligence for monitoring hand hygiene compliance in healthcare settings: A scoping review

Lin, X., Lv, Y., Xiang, Q., Cai, M., & Wang, P. (2026) · PLoS ONE · doi:10.1371/journal.pone.0347683 · Cited by 2

A Comparative Evaluation of Large Language Models on Pediatric Board-Style Examinations

Schönberg, N., Deschler, D. J. P., Hauer, J., & Zeumer, M. (2026) · Hospital Pediatrics · doi:10.1542/hpeds.2025-009153 · Cited by 1

AI-integrated clinical decision support systems for precision medicine in real-world healthcare

Khakpaki, A., javid, S. A., Farshbaf‐Khalili, A., & Sepehri, H. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01518-3 · Cited by 1

Exploring factors associated with nursing students’ artificial intelligence literacy: insights from a national mixed methods study

Karaçay, P., Yaşar, Ö., Göktaş, P., & Küçükkaya, A. (2026) · BMC Nursing · doi:10.1186/s12912-026-04832-4 · Cited by 1

Beyond Algorithmic Oversight: Internal Morality of Medicine and Meaningful Human Control in AI-Assisted Care

Omeljanciuk, A., Peičius, E., Urbonienė, A., & Urbonas, G. (2026) · Healthcare · doi:10.3390/healthcare14121638 · Cited by 2

Integrating Generative Artificial Intelligence (AI) in Medical Education: A Framework for Preserving Clinical Reasoning

Corral-Gudino, L., Montano, I. H., Díez, I. D. L. T., & Miramontes‐González, J. P. (2026) · Applied Sciences · doi:10.3390/app16125946 · Cited by 1

Evidence-like error and physician responsibility in retrieval-augmented clinical artificial intelligence

Bello, F. D., & Angioni, D. (2026) · AboutOpen · doi:10.33393/ao.2026.3822 · Cited by 2

A Futures Framework for Clinical AI Governance: Anticipating Emerging Risks, Shifting Roles, and Regulatory Challenges

Yang, Y., Liu, J., & Liu, S. (2026) · Journal of Medical Internet Research · doi:10.2196/96152 · Cited by 2

The epistemic risk of artificial intelligence over-reliance in clinical medicine: Why productive uncertainty matters

Srivatsa, S. S. (2026) · JVS-Vascular Insights · doi:10.1016/j.jvsvi.2026.100492 · Cited by 1

The Dependency Dilemma: How Machine Learning Decision Aids can Undermine Skill Growth

Bauer, K., Nofer, M., Henrich, B., Drachsler, H., & Hinz, O. (2026) · Business & Information Systems Engineering · doi:10.1007/s12599-026-01014-z · Cited by 1

Ambient scribes in general practice — help or hindrance?

Ladds, E. C., Barry, E., Dixon, S., & Dakin, F. (2026) · British Journal of General Practice · doi:10.3399/bjgp.2026.0097 · Cited by 1

Determinants of Reader-LLM Interaction in Thoracic Radiology: Impact of Model Confidence and Reader Expertise

Song, J., Jeong, W. G., Han, D. H., Ko, H., Yoon, S. H., Kim, H., Naruetook, P., Leong, W. L., Kim, S. S., Moon, H. E., et al. (2026) · Radiology · doi:10.1148/radiol.253397 · Cited by 1

Transforming Human– AI Collaboration in Emergency Care: The Role of AI in Adaptive Support Across Diagnostic Reasoning Modes

Greengrass, C. J., Clancy, M. J., Campbell, S., & Croskerry, P. (2026) · Academic Emergency Medicine · doi:10.1111/acem.70398 · Cited by 1

Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care

Toma, M., & YUSUPOV, D. (2026) · Bioengineering · doi:10.3390/bioengineering13091034 · Cited by 1

Literacy and Language Barriers to Overcome in Laboratory Medicine

Lazaro, G., Taillepierre, J. D., & Richwine, C. (2024) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2024.07.002 · Cited by 1

Forecasting Prescription Efficacy

Yao, H.-R., Mencer, O., Chiang, H., Chang, D., & Frieder, O. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-88720-8_14 · Cited by 1

Explainable AI: Ethical Frameworks, Bias, and the Necessity for Benchmarks

Verhoeven, R., Bouisaghouane, W., & Hulscher, J. B. F. (2025) · European Journal of Pediatric Surgery · doi:10.1055/a-2702-1843 · Cited by 2

Current status of VTE risk assessment and prevention using clinical decision support system: a cross-sectional survey from ChinaPreprint

Xia, L., Zhen, K., Chen, Z., Liang, R., Zhang, X., Gao, Q., Sheng, M., Liu, B., Xia, J., Si, C., et al. (2024) · Research Square · doi:10.21203/rs.3.rs-5008620/v1 · Cited by 1

Human-Centered Design of an Artificial Intelligence (AI) Monitoring System: The Vanderbilt Algorithmovigilance Monitoring and Operations System (VAMOS)Preprint

Salwei, M. E., Davis, S. E., Reale, C., Novak, L. L., Walsh, C. G., Beebe, R., Nelson, S., Sundrani, S. C., Rose, S. L., Wright, A. T., et al. (2025) · medRxiv · doi:10.1101/2025.06.05.25329034 · Cited by 1

Auditor Models to Suppress Poor AI Predictions Can Improve Human-AI Collaborative PerformancePreprint

Brown, K. E., Wrenn, J. O., Jackson, N. J., Cauley, M. R., Collins, B. X., Novak, L. L., Malin, B., & Ancker, J. S. (2025) · medRxiv · doi:10.1101/2025.06.24.25330212 · Cited by 1

Safety of Patient-Facing Agentic AI: a Consensus Framework for Risk Assessment and Mitigation (Preprint)Preprint

Jayaraman, S., Kumar, A. R., Palm, E., Fernando, M., Sarabu, C. R., Choma, M. A., Wang, K., Ravi, V., Suarez, M., McCaffrey, P., et al. (2026) · doi:10.2196/preprints.105596 · Cited by 1

Regulating AI in healthcare: a moving target

Ball, M. J., & Vallejos, E. P. (2026) · BMJ · doi:10.1136/bmj-2026-100555 · Cited by 1

Artificial Intelligence to Facilitate SEP-1 Measure Compliance and Fluid Management in Sepsis

Nguyen, H. B., Krishtopaytis, E., Lopez, E., Farnoudi, N., Van, T., Kharalampova, V., & Coz Yataco, A. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15093477

Does facilitating trust calibration for artificial-intelligence-driven differential diagnoses list improve physicians’ diagnostic accuracy?: A quasi-experimental study (Preprint)Preprint

Sakamoto, T., Harada, Y., & Shimizu, T. (2024) · doi:10.2196/preprints.58666

Other possible perspectives for solving the negative outcome penalty paradox in the application of artificial intelligence in clinical diagnostics

Ye, H. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2024-109968

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 (English Edition) · doi:10.1016/j.gastre.2024.04.026

Balancing Innovation and Safety in Digital Healthcare

Sharma, S., Singh, M. P., & Pradhan, K. B. (2025) · Global Clinical Engineering Journal · doi:10.31354/globalce.v7i1.262

Guideline-driven clinical decision support for colonoscopy patients using the hierarchical multi-label deep learning method

Wu, J., Chen, J., Zhang, H., Luan, Z., Zhao, Y., Sun, M., Wang, S., Li, C., Zhao, Z. J., Zhang, W., et al. (2025) · Chinese Medical Journal · doi:10.1097/cm9.0000000000003469

Exploring the Impact of Cognitive Biases on StockInvestment Choices: A Scale DevelopmentPerspective

Shukla, S., & Shukla, S. K. (2025) · European Journal of Business Management and Research · doi:10.24018/ejbmr.2025.10.4.2749 · Cited by 1

Artificial intelligence-guided point-of-care ultrasonography for cardiomyopathy detection – Authors' reply

Oikonomou, E. K., & Khera, R. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.100893

Adoption of AI-based predictive testing: Disentangling acceptability from confidencePreprint

Renaud Bourlès, A., Jelovac, I., Rafaï, I., & Ventelou, B. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5557538

Introduction to Social Psychology in Action : Addressing the Critical Issues of Our Time and Implications of Removing AI Safeguards

Ma, D. S. (2025) · Social and Personality Psychology Compass · doi:10.1111/spc3.70105

Towards Human-Centered Digital Health Interventions

Kostick, K. M., & Storch, E. A. (2025) · Psychiatric Clinics of North America · doi:10.1016/j.psc.2025.10.004

Bridging the Implementation Gap in Medical AI Education: A Three-Lever Framework for Concurrent Reform (Preprint)Preprint

Kelly, S. L., Walker, M. C., & Forgie, M. (2025) · doi:10.2196/preprints.88778

Person-Centred and Personalised Care for Radiography in the AI Era

Hewis, J., Hargan, J., Strudwick, R. M., & dos Reis, C. S. (2026) · Springer eBooks · doi:10.1007/978-3-032-05080-9_12

Advances in Spinal Surgery: From 3D Printing to Augmented Reality and Artificial Intelligence—A Surgeon’s Perspective on the Future Outlook

Yahanda, A. T., Krishnan, A., Joseph, K., & Molina, C. A. (2026) · Springer eBooks · doi:10.1007/978-981-96-7448-0_38-1

Exposing the fragility of LLM reasoning through bias-inducing prompts: evidence from BiasMedQA

Kim, S. H., Ziegelmayer, S., Busch, F., Mertens, C. J., Keicher, M., Adams, L. C., Bressem, K. K., Braren, R., Makowski, M. R., Kirschke, J. S., et al. (2026) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000189

Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge

Cho, I., Shim, S., & Park, H. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag009

Cognitive Overload by Algorithm: Simulation Showed AI Is Not Ready to Lead Diabetic Ketoacidosis

Monzani, A., Barra, F. L., Scaramuzza, A. E., & Rabbone, I. (2026) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70144

Future directions in infertility research: the role of generative AI and large language models

Jamil, H. M. (2026) · Systems Biology in Reproductive Medicine · doi:10.1080/19396368.2026.2627889

Evolving and novel applications of artificial intelligence in interventional oncology

Almashni, S. Y., Fayek, F. B., Javens, D. C., Boulis, M. T., & Makary, M. S. (2026) · World Journal of Clinical Oncology · doi:10.5306/wjco.v17.i3.113226

Invited Commentary: Artificial Intelligence–Assisted Pediatric Trauma Triage: Promising Innovation or Premature Implementation?

Zakrison, T. L., Poulson, M. R., & Richardson, J. (2026) · Journal of the American College of Surgeons · doi:10.1097/xcs.0000000000001951

Integrating digital health and remote monitoring: emerging trends in cardiac rehabilitation research for chronic heart failure

Gao, C.-M., Fu, P., Du, W., & Li, Y. S. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1774181

Faster but Not Smarter? Temporal Constraints and User Compliance with AI Explanations

Shahu, A., Greinecker, N., & Wintersberger, P. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798697

A multi-cohort evaluation of a curriculum-aligned, oncology-focused chatbot in pharmacy education

Halford, Z., & Heitz, N. (2026) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2026.102659

Management and Prediction of Acute Pancreatitis Severity Using AI: A Surgical Perspective

Dumitrascu, I., Zărnescu, N. O., Marchegiani, G., Ilie, A., Zarnescu, E. C., & Costea, R. (2026) · Diagnostics · doi:10.3390/diagnostics16091350

Leveraging Artificial Intelligence for Personalized Pharmacotherapy: Emerging Roles for Pharmacists

Quero, A., Knoebel, R. W., Tahmasebi, A., & Bastow, S. S. (2026) · Journal of Pharmacy Practice · doi:10.1177/08971900261450457

Shifts in Pulmonary Nodule Detection After Stopping AI Assistance: A Retrospective Repeated-Measures Study

Wu, J., Xiang, J., Kihaga, G. M., Zheng, Y., Pan, C., & Hou, N. (2026) · Iranian Journal of Radiology · doi:10.5812/iranjradiol-169896

Designing Trustworthy and Explainable Clinical Decision Support Systems

Maleki, D. N. M., Zamanifar, A., Farhadi, A., & Sorouri, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_5

The Evolving Utility of Artificial Intelligence-Based Tools for the Detection of Heart Failure and Cardiomyopathies: From Potential to Implementation

Croon, P. M., Dhingra, L. S., Pedroso, A. F., & Khera, R. (2026) · Current Heart Failure Reports · doi:10.1007/s11897-026-00764-x · Cited by 1

Are Existing AI Laws Sufficient to Address Emerging Threats in Healthcare AI? (Preprint)Preprint

Singh, H., Sr., Rana, S., Bhadada, S., Duggal, P., Goel, P., Bhagat, P., & Balsari, S. (2026) · doi:10.2196/preprints.100411

Agentic Artificial Intelligence in Dentistry: A Practice-Focused Perspective for the Dental Team

Farooqi, O. A., DeNucci, D. J., & Moharrami, M. (2026) · European Journal of General Dentistry · doi:10.1055/s-0046-1822820

Inter-rater reproducibility in medico-legal injury assessment: a pilot study with an exploratory comparison to a large language model

Blandino, A., Pacchioni, F., Muccino, E. A., Tambone, V., Micco, F. D., Antonini, C., Zoja, R., & Travaini, G. V. (2026) · International Journal of Legal Medicine · doi:10.1007/s00414-026-03842-w

Prior Authorization and Clinical Authority: Time to Recalibrate?

Weiland, G. (2026) · Annals of Internal Medicine · doi:10.7326/annals-26-00918

Profiles of Generative AI Use and Perceptions Among Health Professional Students During Clinical Placements: Secondary Analysis of a Cross-Sectional Online Survey (Preprint)

Kotzki, S., Gauthier, K., Minovès, M., & Vuillerme, N. (2026) · JMIR Medical Education · doi:10.2196/103267

Transforming hemodialysis care: a tripartite collaboration model among medical staff, AI agents, and robots

Noda, R., Sakurada, T., Ichikawa, D., & Shibagaki, Y. (2026) · Clinical and Experimental Nephrology · doi:10.1007/s10157-026-02903-z

[R.H.J.M.K. and S.M.H. contributed equally to this work.]

Zöller, N., Berger, J., Lin, I., Fu, N., Komarneni, J., Barabucci, G., Laskowski, K., Shia, V., Harack, B., Chu, E., et al. (2025) · MPG.PuRe (Max Planck Society)

IA et management hospitalier : gouverner, définir, intégrer, piloter et évaluer

Staccini, P. (2026) · Soins Cadres · doi:10.1016/j.scad.2026.03.009

Sustaining High Reliability Amid Artificial Intelligence Adoption in Oncology

Stapleton, S., Nargund, R. S., & Chung, C. D. (2026) · Journal of Clinical Oncology · doi:10.1200/jco-26-00253

AI-Augmented Leadership and Digital Decision-Making Models

Roos, G. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-2541-3.ch002

Pediatric Autism Diagnosis Accuracy and Confidence: A Comparison of Experienced and Inexperienced Clinicians Making Decisions with and without AI Decision SupportPreprint

Gondy Leroy, A., Lee, S., Thummanapelly, K. P., Burleson, W., Maltman, N., Rice, S. A., & Rothman, J. M. (2026) · Research Square · doi:10.21203/rs.3.rs-9546356/v1

Artificial intelligence in robot-assisted radical prostatectomy: From technical innovation to clinical translation

Ran, B., Qiu, S., Zhang, M., Chen, Z. Y., Chen, B., Li, J., Bai, J., Chen, J., Jian, B., Chen, S., et al. (2026) · Intelligent Oncology · doi:10.1016/j.intonc.2026.100069

The paradox of intelligence failures in the age of artificial intelligence

Lonergan, E. D. (2026) · Intelligence & National Security · doi:10.1080/02684527.2026.2690392

Moral diversity and the challenge of responsibility in AI-CDSS

Liedtke, W., & Langanke, M. (2026) · Philosophy Ethics and Humanities in Medicine · doi:10.1186/s13010-026-00226-1 · Cited by 1

Evaluating and Understanding Model Editing for Medical Vision Language Models

Zhu, G., Wu, C. E., & Shen, L. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37362-5_8

Beyond the algorithm: nursing as a core foundation for ethical and equitable AI in healthcare

Sun, Y., Li, Y. D., Liu, Y., & Yu, S. (2026) · Cognition Technology & Work · doi:10.1007/s10111-026-00886-1

Die Strukturierte Informationssammlung als Datenbasis für KI-Anwendungen: Eine qualitative Studie zur Datenqualität

Gebert, A., Linnemann, G., Brünett, M., Steffny, L., & Weidner, F. (2026) · Pflege · doi:10.1024/1012-5302/a001084

Artificial Intelligence and Clinician Burnout in the United States: A Narrative Review

Sucharitrak, C. (2026) · Cureus · doi:10.7759/cureus.112814

Clinical cognition in the age of cardiovascular AI

Krieger, J. E. (2026) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2026.1872551

Potential risks of generative artificial intelligence in kidney care

Viera, E., de Arizón, L. F., Torrá, R., & de Maeztu, G. (2026) · Clinical Kidney Journal · doi:10.1093/ckj/sfag200

A fuzzy–blockchain framework for crowdsourced fund allocation (FB-CFA) to startup MSMEs

Bhattacharjee, S. (2026) · Journal of Ethics in Entrepreneurship and Technology · doi:10.1108/jeet-03-2026-0013

Die Mensch-Achse: Input, Kognition und Handlung

Elsharkawy, A. E. (2026) · Essentials · doi:10.1007/978-3-662-74123-8_4

Automation dependence in AI-assisted colonoscopy: impact of system reliability and surgeon experience on navigation performance

Tong, M., Cui, L., Zhu, S., Zhang, X., & Liu, H. (2026) · Surgical Endoscopy · doi:10.1007/s00464-026-13155-z

When Advice Overrides Reasoning: Diagnostic Deference to AI- and Faculty-Labelled Recommendations in Undergraduate Dental EducationPreprint

Šurdilović, D., Shetty, R. M., Azim, S. A., Awadalkreem, F., & Siriwardena, B. S. M. S. (2026) · Research Square · doi:10.21203/rs.3.rs-10208256/v1

Formative Modeling of Multi-Agent AI-Based AR Decision Support for Rural Emergency Department Triage

Kim, J., Sealock, K., Tellier, L., & Park, J. (2026) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/10711813261475195

Artificial intelligence must not worsen maternal health inequalities

Ahmad, M., Prabhu, K., Oakeshott, P., Ryan, R., & Razai, M. S. (2026) · Journal of the Royal Society of Medicine · doi:10.1177/01410768261474986

Trust Is Not a Single Outcome in Pharmacy AI: A Realist Review of Acceptance, Reliance, and Refusal

Nilsson, P., Johansson, E., & Andersson, L. (2025) · Archives of Pharmacy Practice · doi:10.51847/rzro3hilbk

Clinical Applications, Opportunities, and Implementation Challenges of AI in Emergency Medicine: A Narrative Review

Ashiq, N., Munir, F., & Yousaf, A. (2026) · Cureus · doi:10.7759/cureus.114581 · Cited by 1

Performance of an Ambient Generative AI Documentation Tool in a Linguistically Diverse Clinical SettingPreprint

Aldis, R., Wang, S., Sage, M., Metzmaker, M., & Galvin, H. K. (2026) · medRxiv · doi:10.64898/2026.08.14.26360467

A Model-Driven Methodology for Embedding AI Bias Mitigation Requirements into SYSML: Application to Smart Home Early Chronic Kidney Disease (CKD) Systems

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