Algorithmovigilance—Advancing methods to analyze and monitor artificial intelligence-driven health care for effectiveness and equity

Dissecting racial bias in an algorithm used to manage the health of populations

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works

Artificial intelligence in healthcare

Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works

How Structural Racism Works — Racist Policies as a Root Cause of U.S. Racial Health Inequities

Bailey, Z. D., Feldman, J. M., & Bassett, M. T. (2021) · New England Journal of Medicine · doi:10.1056/nejmms2025396 · Cited by 1,598 · see citing works

Addressing bias in artificial intelligence in health care

Parikh, R. B., Teeple, S., & Navathe, A. S. (2019) · JAMA · doi:10.1001/jama.2019.18058 · Cited by 764 · see citing works

Evaluating artificial intelligence in medicine: phases of clinical research

Park, Y., Jackson, G. P., Foreman, M. A., Gruen, D., Hu, J., & Das, A. K. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa033 · Cited by 150 · see citing works

Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression

Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa‐Mullan, I., Koski, E., & Das, A. K. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.3909 · Cited by 116 · see citing works

Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper

Petersen, C. C., Smith, J. R. L., Freimuth, R. R., Goodman, K. W., Jackson, G. P., Kannry, J. L., Liu, H., Madhavan, S., Sittig, D. F., & Wright, A. T. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa319 · Cited by 66 · see citing works

Action-Informed Artificial Intelligence—Matching the Algorithm to the Problem

Lindsell, C. J., Stead, W. W., & Johnson, K. B. (2020) · JAMA · doi:10.1001/jama.2020.5035 · Cited by 56 · see citing works

Toward a responsible future: recommendations for AI-enabled clinical decision support

Labkoff, S., Oladimeji, B., Kannry, J., Solomonides, A., Leftwich, R., Koski, E., Joseph, A. L., Lopez-Gonzalez, M., Fleisher, L. A., Nolen, K., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae209 · Cited by 252 · see citing works

The medical algorithmic audit

Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00003-6 · Cited by 240 · see citing works

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 · see citing works

Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines

Liu, S., McCoy, A. B., & Wright, A. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf008 · Cited by 200 · see citing works

Defining AMIA’s artificial intelligence principles

Solomonides, A. E., Koski, E., Atabaki, S. M., Weinberg, S., McGreevey, J. D., Kannry, J. L., Petersen, C., & Lehmann, C. U. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac006 · Cited by 121 · see citing works

Potential of large language models in health care: Delphi study

Denecke, K., May, R., LLMHealthGroup, & Rivera Romero, O. (2024) · Journal of Medical Internet Research · doi:10.2196/52399 · Cited by 111 · see citing works

Artificial Intelligence for Disease Assessment in Inflammatory Bowel Disease: How Will it Change Our Practice?

Stidham, R. W., & Takenaka, K. (2022) · Gastroenterology · doi:10.1053/j.gastro.2021.12.238 · Cited by 108 · see citing works

Advancing AI in healthcare: A comprehensive review of best practices

Polevikov, S. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117519 · Cited by 112 · see citing works

Risk assessment tools for osteoporosis and fractures in 2022

CAREY, J. J., Wu, P. C.-H., & Bergin, D. (2022) · Best Practice & Research Clinical Rheumatology · doi:10.1016/j.berh.2022.101775 · Cited by 104 · see citing works

Equity within AI systems: What can health leaders expect?

Gurevich, E., Hassan, B. E., & Morr, C. E. (2022) · Healthcare Management Forum · doi:10.1177/08404704221125368 · Cited by 108 · see citing works

Sentiment analysis of clinical narratives: A scoping review

Denecke, K., & Reichenpfader, D. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104336 · Cited by 83 · see citing works

Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review

Berkhout, W. E. M., van Wijngaarden, J. J., Workum, J. D., van de Sande, D., Hilling, D. E., Jung, C., Meyfroidt, G., Gommers, D., Buijsman, S. N. R., & van Genderen, M. E. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.22866 · Cited by 75 · see citing works

The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It

Gervasi, S. S., Chen, I. Y., Smith–McLallen, A., Sontag, D. A., Obermeyer, Z., Vennera, M., & Chawla, R. (2022) · Health Affairs · doi:10.1377/hlthaff.2021.01287 · Cited by 62 · see citing works

Artificial Intelligence In Health And Health Care: Priorities For Action

Matheny, M. E., Goldsack, J. C., Saria, S., Shah, N. H., Gerhart, J. L., Cohen, I. G., Price, W. N., Patel, B., Payne, P., Embí, P. J., et al. (2025) · Health Affairs · doi:10.1377/hlthaff.2024.01003 · Cited by 69 · see citing works

Pharmacovigilance in China: Evolution and future challenges

Song, H., Pei, X., Liu, Z., Shen, C., Sun, J. Y., Liu, Y., Zhou, L., Sun, F., & Xiao, X. (2022) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15277 · Cited by 54 · see citing works

Harnessing the Promise of Artificial Intelligence Responsibly

Dorr, D. A., Adams, L., & Embí, P. (2023) · JAMA · doi:10.1001/jama.2023.2771 · Cited by 49 · see citing works

A normative framework for artificial intelligence as a sociotechnical system in healthcare

McCradden, M. D., Joshi, S. D., Anderson, J. A., & London, A. J. (2023) · Patterns · doi:10.1016/j.patter.2023.100864 · Cited by 49 · see citing works

The promise of data science for health research in Africa

Adebamowo, C., Callier, S., Akintola, S., Maduka, O. C., Jegede, A. S., Arima, C. G., Ogundiran, T. O., & Adebamowo, S. N. (2023) · Nature Communications · doi:10.1038/s41467-023-41809-2 · Cited by 49 · see citing works

Sustainable deployment of clinical prediction tools—a 360° approach to model maintenance

Davis, S. E., Embí, P. J., & Matheny, M. E. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae036 · Cited by 46 · see citing works

Clinical use of artificial intelligence requires AI-capable organizations

Novak, L. L., Russell, R. G., Garvey, K., Patel, M., Thomas Craig, K. J., Snowdon, J., & Miller, B. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad028 · Cited by 43 · see citing works

The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities – a scoping review

Hussain, S. A., Bresnahan, M., & Zhuang, J. (2024) · Ethnicity and Health · doi:10.1080/13557858.2024.2422848 · Cited by 48 · see citing works

Algorithmovigilance, lessons from pharmacovigilance

Balendran, A., Benchoufi, M., Evgeniou, T., & Ravaud, P. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01237-y · Cited by 34 · see citing works

What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning

Mccradden, M., Odusi, O., Joshi, S., Akrout, I., Ndlovu, K., Glocker, B., Maicas, G., Liu, X., Mazwi, M., Garnett, T., et al. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3593013.3594096 · Cited by 33 · see citing works

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Han, L., Char, D. S., Aghaeepour, N., & The Stanford Anesthesia AI Working Group (2024) · Anesthesiology · doi:10.1097/aln.0000000000005013 · Cited by 27 · see citing works

Responsible Artificial Intelligence governance in oncology

Stetson, P. D., Choy, J., Summerville, N., Baldwin-Medsker, A. E., Mak, J., Chatterjee, A., Kim, K. K. O., Kumar, C., Samedy, P., Halperin, J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01794-w · Cited by 26 · see citing works

Embracing Generative Artificial Intelligence in Clinical Research and Beyond

Foote, H. P., Hong, C., Anwar, M. M., Borentain, M., Bugin, K., Dreyer, N. A., Fessel, J. P., Goyal, N., Hanger, M., Hernandez, A. F., et al. (2025) · JACC Advances · doi:10.1016/j.jacadv.2025.101593 · Cited by 30 · see citing works

Evaluating the evidence: a systematic review of reviews of the effectiveness and safety of digital interventions for ADHD

Gabarrón, E., Denecke, K., & López–Campos, G. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06825-0 · Cited by 27 · see citing works

Launching the Trustworthy and Responsible AI Network (TRAIN)

Embí, P. J., Rhew, D. C., Peterson, E. D., & Pencina, M. J. (2025) · JAMA · doi:10.1001/jama.2025.1331 · Cited by 21 · see citing works

Confronting the Disruption of the Infectious Diseases Workforce by Artificial Intelligence: What This Means for Us and What We Can Do About It

Langford, B. J., Branch‐Elliman, W., Nori, P., Marra, A. R., & Bearman, G. M. (2024) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofae053 · Cited by 20 · see citing works

Revolutionizing antimicrobial stewardship, infection prevention, and public health with artificial intelligence: the middle path

Marra, A. R., Langford, B. J., Nori, P., & Bearman, G. M. (2023) · Antimicrobial Stewardship & Healthcare Epidemiology · doi:10.1017/ash.2023.494 · Cited by 19 · see citing works

The Role of Artificial Intelligence in Chronic Liver Diseases and Liver Transplantation

Spann, A., Strauss, A. T., Davis, S. E., & Bhat, M. (2025) · Gastroenterology · doi:10.1053/j.gastro.2025.05.012 · Cited by 21 · see citing works

Toward governance of artificial intelligence in pediatric healthcare

Richter, F., Holmes, E., Richter, F., Guttmann, K., Duong, S. Q., Gangadharan, S., Schadt, E., Salmasian, H., Gelb, B. D., & Glicksberg, B. S. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02000-7 · Cited by 24 · see citing works

Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study

Harada, Y., Sakamoto, T., Sugimoto, S., & Shimizu, T. (2024) · JMIR Formative Research · doi:10.2196/53985 · Cited by 17 · see citing works

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

Hersh, W., Hoyt, R. E., Chamberlin, S. R., Ancker, J. S., Gupta, A., & Borlawsky-Payne, T. B. (2023) · Health Systems · doi:10.1080/20476965.2023.2237745 · Cited by 16 · see citing works

Assessing the Potential Risks of Digital Therapeutics (DTX): The DTX Risk Assessment Canvas

Denecke, K., May, R., Gabarrón, E., & López–Campos, G. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13101523 · Cited by 17 · see citing works

Rise of the Machines - Artificial Intelligence in Healthcare Epidemiology

Non, L. R., Marra, A. R., & İnce, D. (2024) · Current Infectious Disease Reports · doi:10.1007/s11908-024-00854-8 · Cited by 16 · see citing works

How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and Qualitative Analysis

Chow, J., Lee, R., & Wu, H. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01493-8 · Cited by 18 · see citing works

Vision-language models in diagnostic imaging: review of technical advances, clinical validation, and practical deployment

Dutta, N., Bose, K., Syailendra, E. A., Chu, L. C. H., & Gupta, P. K. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106227 · Cited by 13 · see citing works

Safety of a large language model-based clinical decision support system in African primary healthcare

Agweyu, A., Mwaniki, P., Musau, W., Korom, R., Isaaka, L., Wanyama, C. W., Kiptinness, S., Adan, N., Emmanuel-Fabula, M., & Mateen, B. A. (2026) · Nature Health · doi:10.1038/s44360-026-00082-5 · Cited by 22 · see citing works

Scalable incident detection via natural language processing and probabilistic language models

Walsh, C. G., Wilimitis, D., Chen, Q., Wright, A. P., Kolli, J., Robinson, K., Ripperger, M. A., Johnson, K. B., Carrell, D., Desai, R. J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-72756-7 · Cited by 13 · see citing works

From Black-Box Clinical AI to Algorithmovigilance: A Minimum-Viable Governance Protocol for Safety, Accountability, and Prevention of Algorithmic Harm

Sirago, G., Vinci, A., Visci, P., Solarino, B., Dell'Erba, A. S., & Ferorelli, D. (2026) · Journal of Medical Systems · doi:10.1007/s10916-026-02382-5 · Cited by 10 · see citing works

Using a Multilingual AI Care Agent to Reduce Disparities in Colorectal Cancer Screening for Higher Fecal Immunochemical Test Adoption Among Spanish-Speaking Patients: Retrospective Analysis

Bhimani, M., Baker, R. H., Ausin, M. S., Meixiong, G., Lasko, R., Raglow-Defranco, M., Miller, A., Mukherjee, S., Godil, S., Cook, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/71211 · Cited by 10 · see citing works

Digital Interventions and Their Unexpected Outcomes – Time for Digitalovigilance?

López–Campos, G., Gabarrón, E., Martin-Sanchez, F. J., Merolli, M., Petersen, C. C., & Denecke, K. (2024) · Studies in health technology and informatics · doi:10.3233/shti231011 · Cited by 9 · see citing works

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 10 · see citing works

Safe and Equitable Pediatric Clinical Use of AI

Handley, J. L., Lehmann, C. U., & Ratwani, R. M. (2024) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2024.0897 · Cited by 9 · see citing works

AI for all: bridging data gaps in machine learning and health

Wang, M. L., & Bertrand, K. A. (2025) · Translational Behavioral Medicine · doi:10.1093/tbm/ibae075 · Cited by 10 · see citing works

Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority

Ong, J. C. L., Ning, Y., Liu, M., Ma, Y., Zhao, L., Singh, K., Chang, R. T., Vogel, S., Lim, J. C. W., Tan, I. S. K., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02552-2 · Cited by 7 · see citing works

Artificial Intelligence, Machine Learning, and Natural Language Processing

Shoenbill, K. A., Kasthurirathne, S. N., & Mendonça, E. A. (2023) · Springer eBooks · doi:10.1007/978-3-031-29171-5_36 · Cited by 7 · see citing works

Learning from data: A recurring feature on the science and practice of data‐driven learning health systems

Embí, P. J., Payne, P., & Friedman, C. P. (2021) · Learning Health Systems · doi:10.1002/lrh2.10302 · Cited by 7 · see citing works

Potential of artificial intelligence in injury prevention research and practice

Quistberg, D. A. (2024) · Injury Prevention · doi:10.1136/ip-2023-045203 · Cited by 6 · see citing works

Artificial intelligence and the future of pharmacy

Nelson, S. D. (2023) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxad316 · Cited by 6 · see citing works

Explainability in context: calibrating appropriate trust and reliance in artificial intelligence

Davis, S. E., & Salwei, M. E. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag082 · Cited by 8 · see citing works

Algorithm development to improve intervention effectiveness for parents with mental health signs and symptoms

Austin, R. R., Laarhoven, E. V., Graefe, A., Huling, J. D., Mathiason, M. A., & Monsen, K. A. (2023) · Public Health Nursing · doi:10.1111/phn.13190 · Cited by 5 · see citing works

Artificial intelligence and the future of the pharmacist’s role in drug information

Patel, R., Shirk, R., Advani, A., Kar, I., Munir, F. M., Nelson, S. D., Saad, M., & Aungst, T. D. (2025) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxaf175 · Cited by 4 · see citing works

Expert-Based, Institutional Approaches for Reducing the Diagnostic Odyssey of Patients With Inborn Errors of Immunity

Rider, N. L., Bastarache, L. A., Anderson, J. T., Behrens, E. M., Chaimowitz, N. S., Chandrakasan, S., Hartline, B. K., Liu, A. Y., Marsh, R. A., & Connelly, J. A. (2025) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2025.03.040 · Cited by 4 · see citing works

Advancing sustainability through digitalization: from drug development to patient care

Eugster, R., & Luciani, P. (2026) · RSC Pharmaceutics · doi:10.1039/d5pm00342c · Cited by 4 · see citing works

Artificial Intelligence in Anesthesiology: Field of Dreams or Fire Swamp? Preemptive Strategies for Optimizing Our Inevitable Future

Salwei, M. E., & Weinger, M. B. (2024) · Anesthesiology · doi:10.1097/aln.0000000000005046 · Cited by 3 · see citing works

The Role of Health Data Utilities in Supporting Health AI

Alkasir, A., Seidman, G., Ritzo, J., Bari, L., & Khurshid, A. (2024) · NEJM AI · doi:10.1056/aipc2400401 · Cited by 3 · see citing works

Using Electronic Health Record Mortality Data to Promote Goals-of-Care Discussions in Seriously Ill Transferred Patients: A Pilot Study

Mahendraker, N., Gutierrez-Asis, E., Park, S., Williams, L. S., Schleyer, T., & Umberfield, E. E. (2024) · ACI Open · doi:10.1055/s-0044-1788652 · Cited by 2 · see citing works

The Future of Artificial Intelligence in Medical Education and Continuing Medical Education

Webber, C. J., Whitworth, P., & Stenner, S. P. (2025) · Primary Care Clinics in Office Practice · doi:10.1016/j.pop.2025.07.010 · Cited by 2 · see citing works

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 · see citing works

Artificial Intelligence in Haemophilia Care: A Narrative Review of Current Evidence and Future Opportunities

Hermans, C. R. J. R. (2026) · Haemophilia · doi:10.1111/hae.70268 · Cited by 2 · see citing works

Healthcare AI as Critical Digital Health Infrastructure: A Public Health Preparedness Framework for Systemic Risk

Lipskiy, N., & Flowerday, S. (2026) · Future Internet · doi:10.3390/fi18050232 · Cited by 2 · see citing works

Extending the Fundamental Theorem of Biomedical Informatics for the AI era

Payne, P., Chen, J. H., & Longhurst, C. (2026) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocag065 · Cited by 2 · see citing works

Alignment of Policy, Practice, and Patient Safety for Trustworthy AI in Radiology

Doo, F. X., Davis, M., Poff, J. A., Lui, Y. W., Haines, K., & Towbin, A. J. (2026) · Radiology Artificial Intelligence · doi:10.1148/ryai.250982 · Cited by 2 · see citing works

Role and Use of Race in Artificial Intelligence and Machine Learning Models Related to Health

Were, M. C., Li, A., Malin, B., Yin, Z., Coco, J., Collins, B. X., Clayton, E. W., Novak, L. L., Hendricks‐Sturrup, R., Oluyomi, A., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/73996 · Cited by 3 · see citing works

Algorithmic biases in mental health diagnoses and their impact on vulnerable populations: a documentary review of advances and challenges

Matos, A. G. M. (2022) · EthAIca · doi:10.56294/ai202220 · Cited by 1 · see citing works

Evaluating the effectiveness of AI-enhanced “One Body, Two Wings” pharmacovigilance models in China: a nationwide survey on medication safety and risk management

Yang, J., Sun, Y., Li, F., Wei, Q., Wei, J., & Zhang, Y. (2025) · Frontiers in Health Services · doi:10.3389/frhs.2025.1655726 · Cited by 1 · see citing works

Harnessing the power of artificial intelligence for clinical trials in cancer

Sönmez, G., Yazarkan, Y., Sahin, T. K., & Güven, D. C. (2026) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2026.2642221 · Cited by 1 · see citing works

Informatics Infrastructure for the Learning Health System

Tenenbaum, J. D., Cimino, J. J., Dorr, D. A., Embí, P. J., Melton, G. B., Ritchie, M. D., Weiner, M. G., & Friedman, C. P. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092624-103225 · Cited by 1 · see citing works

Ethical considerations for multimodal artificial intelligence in healthcare

Kostick, K. M., Wagner, J. K., Cabrera, L. Y., & Mandl, K. D. (2026) · AI and Ethics · doi:10.1007/s43681-026-01259-0 · Cited by 1 · see citing works

The Future of Medical Sentiment Analysis

Denecke, K. (2023) · Springer eBooks · doi:10.1007/978-3-031-30187-2_18 · Cited by 1 · see citing works

Lessons for Approaching Implementation of AI Systems in Clinical Settings

Owoyemi, A., Okpara, E., Okor, M., Awais, F., Harwani, S., Salwei, M. E., & Boyd, A. (2024) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi61247.2024.00067 · Cited by 1 · see citing works

Device-Free Recovery Assessment: A Conceptual Architecture for Mobility-Derived Outcome Inference

Harris, O., & Walker, J. (2023) · Journal of Health Informatics and Digital Systems · doi:10.68159/w432380362 · Cited by 1 · see citing works

Ethical and Policy Issues

Korngiebel, D. M., Solomonides, A. E., & Goodman, K. W. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-09108-7_18 · Cited by 1 · see citing works

Inteligencia artificial en enfermedades infecciosas

Belloso, W. H., Pasterán, F. G., Burgos, V. L., Díaz, S. A., Ciabis, V., & Risk, M. R. (2023) · Actualizaciones en Sida e Infectología · doi:10.52226/revista.v31i112.209

Codified Racism in Digital Health Platforms A Meta-Analysis of COVID-19 Prediction Algorithms and their Policy ImplicationsPreprint

Hislop, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3249460/v1 · Cited by 1 · see citing works

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 · see citing works

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 · see citing works

Retrospective Evaluation of a Generative AI-Enabled Electronic Medical Record System in Primary Health Care Facilities in KenyaPreprint

Agweyu, A., Mwaniki, P., Musau, W., Korom, R., Isaaka, L., Wanyama, C. W., Kiptinness, S., Adan, N., Emmanuel-Fabula, M., & Mateen, B. A. (2025) · medRxiv · doi:10.1101/2025.09.05.25335163 · Cited by 1 · see citing works

Bridging the Gap: Consensus-Based Considerations for AI Usefulness in Healthcare

Salwei, M. E., Morse, K., Saria, S., Shah, N. H., Bedoya, A., Beyer, M., del Río, A. M., Chornenky, D., Lin, A. L., Ruparel, S., et al. (2026) · The American Journal of Bioethics · doi:10.1080/15265161.2026.2617850 · Cited by 1 · see citing works

The AI Hospital Formulary: A Practical Governance Framework for Prescribing, Monitoring, and Deprescribing Artificial Intelligence in Hospitals

Epelde, F. (2026) · Hospitals · doi:10.3390/hospitals3030015

Data access, data bias, data equity

Shung, D. L., & Laine, L. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95068-8.00002-9

The Impact of Technological Progress on Pharmacovigilance

Yang, J., & Li, F. (2025) · Pharmaceutical science. · doi:10.5772/intechopen.1006906

Implementing Generative AI into Health Systems, Hospitals and Your Practice: Preparing for Change

Urban, H. (2025) · Springer eBooks · doi:10.1007/978-3-031-83526-1_15

Navigating Data Diversity and Equity in Healthcare with AI

Rubeis, G. (2026) · Health informatics · doi:10.1007/978-3-032-11938-4_10

Community medical centers struggle to produce well-calibrated clinical prediction models: Data augmentation can help

Brown, K. L., Malin, B., & Davis, S. E. (2026) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2026.105012

Building Toward a Future for Electronic Health Record Systems

Rosenbloom, S. T., Holmgren, A. J., & Steitz, B. D. (2026) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-092724-050949

Trust, Verify, Override: Behavioral Governance for Generative Artificial Intelligence in Medical Imaging.

Glenning, J., & Gualtieri, L. (2026) · Health Education & Behavior · doi:10.1177/10901981261451801 · Cited by 1 · see citing works

The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems

Bian, J., Afshar, M., Scifres, C., Webber, E. C., Burton, D., Vawdrey, D., Wang, F., Melton, G. B., Shah, N. H., Patzer, R. E., et al. (2026) · npj Health Systems · doi:10.1038/s44401-026-00107-x

Structural Inequity in Digital Health: A Compounding Model of Algorithmic Bias Across the Data–Model–Deployment–Governance Pipeline

Nayak, K. (2026) · Journal of Public and Clinical Health Research · doi:10.63486/jpchr.26060801

The Model Evidence Dossier for Traceable, Adaptive, and Change-Controlled Artificial Intelligence in Pharmaceutical Development

Ramirez, C., Torres, E., Ortega, P., & Mendes, S. (2026) · Pharmacophore · doi:10.51847/2pdevvb1uu

Governing Artificial Intelligence after Deployment in the Medication-Use System

Osei, D., Afriyie, A., & Adu, K. (2025) · Archives of Pharmacy Practice · doi:10.51847/hpznblgkmc

Algorithmovigilance in AI-Based Oral-Health Surveillance: Temporal Drift, Cross-Survey Differences, and Predictor-Level Structural Stability in Population-Level Severe Tooth Loss Prediction

Lam, Q. T., Fan, F.-Y., Wang, Y., Feng, S., Vo, T. T. T., Minh, L. H. N., Vu, G. T., Le, N. Q. K., & Lee, I. (2026) · Diagnostics · doi:10.3390/diagnostics16162569

Continual Learning without Silent Change in Medication-Support Systems

Kim, D., Park, J., Kang, M.-S., & Jung, H.-W. (2026) · Archives of Pharmacy Practice · doi:10.51847/65q1pxmaoe

Who Governs the Digital Pharmacy Algorithm? An Integrative Review of Accountability, Regulation, and Professional Practice

Fedorova, E., Volkov, A., Morozova, I., & Kuznetsov, D. A. (2026) · Archives of Pharmacy Practice · doi:10.51847/rjpzvoq1ql

Large Language Models as Raters of Hospital Incident Reports: A Blinded Multi-Model Evaluation of Severity Under-Grading and Shared Errors

Sirago, G., Sabato, S., Sebastiani, N., Zotti, F., Dell'Erba, A., & Ferorelli, D. (2026) · The Joint Commission Journal on Quality and Patient Safety · doi:10.1016/j.jcjq.2026.09.002

Diagnostic performance and energy consumption of deep learning models for dermoscopic image classification

Ecer, N., Kankılıç, A. T., Karakoyun, Ö., Aslancan, B., & Kaldan, B. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02558-5

An analytics framework for timing-aware evaluation of prediction-triggered clinical alerts

Toffaha, K. M., de Saad, A. M. S., Simsekler, M. C. E., Kortt, M. A., & Chapman, J. (2026) · Healthcare Analytics · doi:10.1016/j.health.2026.100504

Algorithmic Bias and Fairness in Medical AI: Ensuring Equity in Data-Driven Healthcare Innovation

Lawal, G. S. (2025) · doi:10.13140/rg.2.2.14908.91527

Ensuring AI Algorithm Fairness in Healthcare Decision-Making

Mensah, G. B. (2023) · doi:10.13140/rg.2.2.13354.22727 · Cited by 1 · see citing works

Design and Early Evaluation of an Algorithmovigilance System

Salwei, M. E., Davis, S. E., Novak, L. L., Walsh, C., Reale, C., Nelson, S., Rose, S., & Embí, P. J. (2026) · Studies in health technology and informatics · doi:10.3233/shti260279

Assessing Fairness in AI-enabled Public Health Surveillance

Hohma, E. (2021) · doi:10.13140/rg.2.2.11318.57929 · Cited by 2 · see citing works

Artificial Intelligence in Personalized Medicine: Ensuring Algorithmic Fairness, Accountability, and Healthcare Data Security

Fareed, G., & Oscar, E. (2025) · doi:10.13140/rg.2.2.13215.27041