Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril

Smart wearable devices in cardiovascular care: Where we are and how to move forward

Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829

Artificial intelligence for good health: a scoping review of the ethics literature.

Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435

Identifying Ethical Considerations for Machine Learning Healthcare Applications

Char, D. S., Abràmoff, M. D., & Feudtner, C. (2020) · The American Journal of Bioethics · doi:10.1080/15265161.2020.1819469 · Cited by 415

Artificial intelligence in medical education: a cross-sectional needs assessment

Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022) · BMC Medical Education · doi:10.1186/s12909-022-03852-3 · Cited by 363

Competencies for the Use of Artificial Intelligence–Based Tools by Health Care Professionals

Russell, R. G., Lovett Novak, L., Patel, M., Garvey, K. V., Craig, K. J. T., Jackson, G. P., Moore, D., & Miller, B. M. (2023) · Academic Medicine · doi:10.1097/acm.0000000000004963 · Cited by 320

Global regulatory frameworks for the use of Artificial Intelligence (AI) in the healthcare services sector

Palaniappan, K., Lin, E. Y. T., & Vogel, S. (2024) · Healthcare · doi:10.3390/healthcare12050562 · Cited by 301

Human-Centered Design to Address Biases in Artificial Intelligence

Chen, Y., Clayton, E. W., Novak, L. L., Anders, S. H., & Malin, B. (2023) · Journal of Medical Internet Research · doi:10.2196/43251 · Cited by 226

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Bonkhoff, A. K., & Grefkes, C. (2021) · Brain · doi:10.1093/brain/awab439 · Cited by 216

Exploring stakeholder attitudes towards AI in clinical practice

Scott, I. A., Carter, S. M., & Coiera, E. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100450 · Cited by 193

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

Priorities for successful use of artificial intelligence by public health organizations: a literature review

Fisher, S., & Rosella, L. C. (2022) · BMC Public Health · doi:10.1186/s12889-022-14422-z · Cited by 190

Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity

Alami, H., Lehoux, P., Auclair, Y., de Guise, M., Gagnon, M., Shaw, J., Roy, D. A., Fleet, R. P., Ahmed, M. A. A., & Fortin, J. (2020) · Journal of Medical Internet Research · doi:10.2196/17707 · Cited by 197

Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review

(2023) · doi:10.2196/40337 · Cited by 156

Clinician checklist for assessing suitability of machine learning applications in healthcare

Scott, I. A., Carter, S. M., & Coiera, E. W. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100251 · Cited by 143

Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks

Crossnohere, N. L., Elsaid, M. I., Paskett, J., Bose‐Brill, S., & Bridges, J. F. P. (2022) · Journal of Medical Internet Research · doi:10.1016/j.jval.2022.04.1285 · Cited by 148

Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study

Shevtsova, D., Ahmed, A., Boot, I. W. A., Sanges, C., Hudecek, M., Jacobs, J. J. L., Hort, S., & Vrijhoef, H. J. M. (2024) · JMIR Human Factors · doi:10.2196/47031 · Cited by 124

A sociotechnical systems framework for the application of artificial intelligence in health care delivery

Salwei, M. E., & Carayon, P. (2022) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/15553434221097357 · Cited by 112

Regulating AI in Mental Health: Ethics of Care Perspective

Tavory, T. (2024) · JMIR Mental Health · doi:10.2196/58493 · Cited by 108

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 121

Simulation-based approaches for drug delivery systems: Navigating advancements, opportunities, and challenges

Salahshoori, I., Golriz, M., Nobre, M. A. L., Mahdavi, S., Malekshah, R. E., Javdani-Mallak, A., Jorabchi, M. N., Khonakdar, H. A., Wang, Q., Mohammadi, A. H., et al. (2023) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2023.123888 · Cited by 109

Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey

Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01586-8 · Cited by 114

How machine learning is embedded to support clinician decision making: an analysis of FDA-approved medical devices

Lyell, D., Coiera, E., Chen, J., Shah, P., & Magrabi, F. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2020-100301 · Cited by 95

Emerging Technologies for Next Generation Remote Health Care and Assisted Living

Ahmad, I., Asghar, Z., Kumar, T., Li, G., Manzoor, A., Mikhaylov, K., Shah, S. A., Höyhtyä, M., Reponen, J., Huusko, J., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3177278 · Cited by 107

Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

Rafiei, A., Zahedifar, R., Sitaula, C., & Marzbanrad, F. (2022) · IEEE Access · doi:10.1109/access.2022.3190502 · Cited by 91

Applications of artificial intelligence for nursing: has a new era arrived?

Bulck, L. V., Couturier, R., & Moons, P. (2022) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvac097 · Cited by 73

Artificial Intelligence: Exploring the Future of Innovation in Allergy Immunology

MacMath, D., Chen, M., & Khoury, P. (2023) · Current Allergy and Asthma Reports · doi:10.1007/s11882-023-01084-z · Cited by 70

Artificial intelligence in clinical research of cancers

Shao, D., Dai, Y., Li, N., Cao, X., Zhao, W., Li, C., Rong, Z., Huang, L., Wang, Y., & Zhao, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab523 · Cited by 66

Artificial intelligence in thoracic surgery: past, present, perspective and limits

Etienne, H., Hamdi, S., Le Roux, M., Camuset, J., Khalife-Hocquemiller, T., Giol, M., Debrosse, D., & Assouad, J. (2020) · European Respiratory Review · doi:10.1183/16000617.0010-2020 · Cited by 60

Advancing the Understanding of the Role of Responsible AI in the Continued Use of IoMT in Healthcare

Al-Dhaen, F., Hou, J., Rana, N. P., & Weerakkody, V. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10193-x · Cited by 58

Expectations for Artificial Intelligence (AI) in Psychiatry

Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E. D., & Bauer, M. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01378-5 · Cited by 61

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 74

Modeling Conceptual Framework for Implementing Barriers of AI in Public Healthcare for Improving Operational Excellence: Experiences from Developing Countries

Joshi, S., Sharma, M., Das, R. P., Rosak-Szyrocka, J., Żywiołek, J., Muduli, K., & Prasad, M. (2022) · Sustainability · doi:10.3390/su141811698 · Cited by 64

From Code to Bedside: Implementing Artificial Intelligence Using Quality Improvement Methods

Smith, M., Sattler, A., Hong, G., & Lin, S. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06394-w · Cited by 51

Artificial intelligence and machine learning for hemorrhagic trauma care

Peng, H. T., Siddiqui, M. M., Rhind, S. G., Zhang, J., da Luz, L. T., & Beckett, A. (2023) · Military Medical Research · doi:10.1186/s40779-023-00444-0 · Cited by 53

DEPLOYR: a technical framework for deploying custom real-time machine learning models into the electronic medical record

Corbin, C. K., Maclay, R., Acharya, A., Mony, S., Punnathanam, S., Thapa, R., Kotecha, N., Shah, N. H., & Chen, J. H. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad114 · Cited by 64

Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study

Gal, H. C. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.931225 · Cited by 60

Exploiting the power of information in medical education

Cutrer, W. B., Spickard, W. A., Triola, M. M., Allen, B. L., Spell, N. O., Herrine, S. K., Dalrymple, J. L., Gorman, P., & Lomis, K. D. (2021) · Medical Teacher · doi:10.1080/0142159x.2021.1925234 · Cited by 49

Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology

Ajagbe, S. A., Awotunde, J. B., Adesina, A. O., Achimugu, P., & Kumar, T. A. (2022) · Springer eBooks · doi:10.1007/978-981-16-8150-9_14 · Cited by 49

Preparing Clinicians for a Clinical World Influenced by Artificial Intelligence

James, C. A., Wachter, R. M., & Woolliscroft, J. O. (2022) · JAMA · doi:10.1001/jama.2022.3580 · Cited by 48

Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives

Fazakarley, C.-A., Breen, M. A., Leeson, P., Thompson, B. T., & Williamson, V. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-076950 · Cited by 53

Ethical considerations in design and implementation of home-based smart care for dementia

Hine, C., Nilforooshan, R., & Barnaghi, P. M. (2022) · Nursing Ethics · doi:10.1177/09697330211062980 · Cited by 48

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

Consequence of Resistance to Change on AI Readiness: Mediating–Moderating Role of Task-oriented Leadership and High-Performance Work System in the Hospitality Sector

Li, C., Ashraf, S. F., Amin, S., & Safdar, M. N. (2023) · SAGE Open · doi:10.1177/21582440231217731 · Cited by 55

Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia

Tanioka, T., Yokotani, T., Tanioka, R., Betriana, F., Matsumoto, K., Locsin, R. C., Zhao, Y., Osaka, K., Miyagawa, M., & Schoenhofer, S. O. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18094538 · Cited by 43

How Artificial Intelligence is altering the nursing workforce

Yakusheva, O., Bouvier, M. J., & Hagopian, C. O. P. (2024) · Nursing Outlook · doi:10.1016/j.outlook.2024.102300 · Cited by 62

Demystifying machine learning: a primer for physicians

Scott, I. A. (2021) · Internal Medicine Journal · doi:10.1111/imj.15200 · Cited by 44

Implementing quality management systems to close the AI translation gap and facilitate safe, ethical, and effective health AI solutions

Overgaard, S. M., Graham, M. G., Brereton, T., Pencina, M. J., Halamka, J. D., Vidal, D. E., & Economou-Zavlanos, N. J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00968-8 · Cited by 43

Emerging algorithmic bias: fairness drift as the next dimension of model maintenance and sustainability

Davis, S. E., Dorn, C., Park, D. J., & Matheny, M. E. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf039 · Cited by 43

Physician’s autonomy in the face of AI support: walking the ethical tightrope

Funer, F., & Wiesing, U. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1324963 · Cited by 39

Artificial Intelligence in Medicine: Text Mining of Health Care Workers’ Opinions

Nitiéma, P. (2022) · Journal of Medical Internet Research · doi:10.2196/41138 · Cited by 40

Open questions and research gaps for monitoring and updating AI-enabled tools in clinical settings

Davis, S. E., Walsh, C. G., & Matheny, M. E. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.958284 · Cited by 33

Evaluation of the Rosa Chatbot Providing Genetic Information to Patients at Risk of Hereditary Breast and Ovarian Cancer: Qualitative Interview Study

Siglen, E., Vetti, H. H., Augestad, M., Steen, V. M., Lunde, Å., & Bjorvatn, C. (2023) · Journal of Medical Internet Research · doi:10.2196/46571 · Cited by 33

Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care

Rakers, M. M., van Buchem, M. M., Kucenko, S., de Hond, A., Kant, I. M. J., van Smeden, M., Moons, K. G. M., Leeuwenberg, A., Chavannes, N. H., Villalobos-Quesada, M. J., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.32990 · Cited by 38

Medical Information Mart for Intensive Care: A Foundation for the Fusion of Artificial Intelligence and Real-World Data

Rogers, P. B., Wang, D., & Lu, Z. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.691626 · Cited by 33

Data Interoperability and Harmonization in Cardiovascular Genomic and Precision Medicine

Chahal, C. A. A., Alahdab, F., Asatryan, B., Addison, D., Aung, N., Chung, M. K., Denaxas, S., Dunn, J., Hall, J. L., Pamir, N., et al. (2025) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.124.004624 · Cited by 35

Epistemic Rights and Responsibilities of Digital Simulacra for Biomedicine

Cho, M. K., & Martinez‐Martin, N. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2146785 · Cited by 32

Aligning quality improvement efforts and policy goals in a national integrated health system

Braganza, M. Z., Pearson, E., Avila, C. J., Zlowe, D., Øvretveit, J., & Kilbourne, A. M. (2022) · Health Services Research · doi:10.1111/1475-6773.13944 · Cited by 30

External Validation of the eRADAR Risk Score for Detecting Undiagnosed Dementia in Two Real-World Healthcare Systems

Coley, R. Y., Smith, J. J., Karliner, L., Idu, A., Lee, S. J., Fuller, S., Lam, R., Barnes, D. E., & Dublin, S. (2022) · Journal of General Internal Medicine · doi:10.1007/s11606-022-07736-6 · Cited by 27

Risky business: a scoping review for communicating results of predictive models between providers and patients

Walsh, C. G., McKillop, M. M., Lee, P., Harris, J. W., Simpson, C., & Novak, L. L. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab092 · Cited by 26

Ethics dumping in artificial intelligence

Bélisle-Pipon, J.-C., & Victor, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1426761 · Cited by 26

AI Quality Standards in Health Care: Rapid Umbrella Review

Kuziemsky, C. E., Chrimes, D., Minshall, S. R., Mannerow, M., & Lau, F. (2024) · Journal of Medical Internet Research · doi:10.2196/54705 · Cited by 30

Qualitative ethical technology assessment of artificial intelligence (AI) and the internet of things (IoT) among filipino Gen Z members: implications for ethics education in higher learning institutions

Jabar, M. A., Chiong-Javier, E., & Sherer, P. P. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2303048 · Cited by 36

Application of Virtual Reality, Artificial Intelligence, and Other Innovative Technologies in Healthcare Education (Nursing and Midwifery Specialties): Challenges and Strategies

Georgieva-Tsaneva, G., Serbezova, I., & Beloeva, S. (2024) · Education Sciences · doi:10.3390/educsci15010011 · Cited by 25

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu, R., Abbod, M., Hsiao, F., Chen, W., & Shieh, J.-S. (2022) · Technologies · doi:10.3390/technologies10030074 · Cited by 28

Increasing use of artificial intelligence in genomic medicine for cancer care- the promise and potential pitfalls

O’Connor, O., & McVeigh, T. P. (2025) · BJC Reports · doi:10.1038/s44276-025-00135-4 · Cited by 36

The future of artificial intelligence in thoracic surgery for non-small cell lung cancer treatment a narrative review

Abbaker, N., Minervini, F., Guttadauro, A., Solli, P., Cioffi, U., & Scarci, M. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1347464 · Cited by 25

Leveraging law and ethics to promote safe and reliable AI/ML in healthcare

Drabiak, K. (2022) · Frontiers in Nuclear Medicine · doi:10.3389/fnume.2022.983340 · Cited by 22

A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning

Cagliero, D., Deuitch, N. T., Shah, N. H., Feudtner, C., & Char, D. S. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad022 · Cited by 23

Ten principles for data sharing and commercialization

Cole, C. L., Sengupta, S., Rossetti, S. C., Vawdrey, D. K., Halaas, M., Maddox, T. M., Gordon, G. D., Dave, T., Payne, P., Williams, A. E., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa260 · Cited by 23

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes

Shapiro, M. R., Tallon, E. M., Brown, M. E., Posgai, A. L., Clements, M. A., & Brusko, T. M. (2024) · Diabetologia · doi:10.1007/s00125-024-06339-6 · Cited by 20

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

Morris, A. H., Horvat, C. M., Stagg, B. C., Grainger, D. W., Lanspa, M. J., Orme, J. F., Clemmer, T. P., Weaver, L. K., Thomas, F. O., Grissom, C. K., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac143 · Cited by 21

Engaging Multidisciplinary clinical users in the design of an artificial intelligence-powered graphical user interface for intensive care unit instability decision support

Helman, S., Terry, M. A., Pellathy, T., Hravnak, M., George, E., Al-Zaiti, S., & Clermont, G. (2023) · Applied Clinical Informatics · doi:10.1055/s-0043-1775565 · Cited by 20

Artificial intelligence in pediatrics: the future is now

Knake, L. A. (2022) · Pediatric Research · doi:10.1038/s41390-022-01972-6 · Cited by 20

Harnessing artificial intelligence for enhanced veterinary diagnostics: A look to quality assurance, Part I Model development

Pacholec, C., Flatland, B., Xie, H., & Zimmerman, K. (2024) · Veterinary Clinical Pathology · doi:10.1111/vcp.13401 · Cited by 19

The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment

Khan, M., Banerjee, S., Muskawad, S., Maity, R., Chowdhury, S. R., Ejaz, R., Kuuzie, E., & Satnarine, T. (2024) · Current Allergy and Asthma Reports · doi:10.1007/s11882-024-01152-y · Cited by 19

Forecasting the future of smart hospitals: findings from a real-time delphi study

Jovy-Klein, F., Stead, S., Salge, T. O., Sander, J., Diehl, A., & Antons, D. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11895-z · Cited by 27

AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation

Okamoto, S., Kataoka, M., Itano, M., & Sawai, T. (2025) · BMC Medical Education · doi:10.1186/s12909-025-06801-y · Cited by 18

Normativity and AI moral agency

Zafar, M. (2024) · AI and Ethics · doi:10.1007/s43681-024-00566-8 · Cited by 26

Ethical Challenges of Artificial Intelligence in Health Care: A Narrative Review

Hui, A. T., Ahn, S. S., Lye, C. T., & Deng, J. (2021) · Ethics in Biology Engineering and Medicine An International Journal · doi:10.1615/ethicsbiologyengmed.2022041580 · Cited by 20

Machine Learning and the Pursuit of High-Value Health Care

Ganguli, I., Gordon, W. J., Lupo, C., Sands-Lincoln, M., George, J., Jackson, G. P., Rhee, K. B. L., & Bates, D. W. (2020) · NEJM Catalyst · doi:10.1056/cat.20.0094 · Cited by 17

Perception of Medical Undergraduates on Artificial Intelligence in Medical Education: Qualitative Exploration

Seneviratne, T., Kodikara, K. G., Abeykoon, I., & Palpola, W. (2025) · JMIR Medical Education · doi:10.2196/73798 · Cited by 24

The Future of Thyroid Nodule Risk Stratification

Burgos, N., Ospina, N. S., & Sipos, J. A. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2021.12.002 · Cited by 16

Restoring Trust: The Need for Precision Medicine in Infectious Diseases, Public Health and Vaccines

Equils, O., Bakaj, A., Wilson-Mifsud, B., & Chatterjee, A. (2023) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2023.2234787 · Cited by 15

Prediction of Important Factors for Bleeding in Liver Cirrhosis Disease Using Ensemble Data Mining Approach

Aleksić, A., Nedeljković, S., Jovanović, M., Ranđelović, M., Vuković, M., Stojanović, V. S., Radovanović, R., Ranđelović, M., & Ranđelović, D. M. (2020) · Mathematics · doi:10.3390/math8111887 · Cited by 16

Bridging Artificial Intelligence and Medical Education: Navigating the Alignment Paradox

Turner, L., Knopp, M. I., Mendonça, E. A., & Desai, S. V. (2025) · ATS Scholar · doi:10.34197/ats-scholar.2024-0086ps · Cited by 17

Artificial intelligence in telemetry: what clinicians should know

Maslove, D. M., Elbers, P., & Clermont, G. (2021) · Intensive Care Medicine · doi:10.1007/s00134-020-06295-w · Cited by 15

A strategic approach to improving pharmacy enterprise automation: Development and initial application of the Autonomous Pharmacy Framework

Flynn, A. J., Fortier, C. R., Maehlen, H., Pierzinski, V., Runnebaum, R., Sullivan, M., Wagner, J., & Stevenson, J. G. (2021) · American Journal of Health-System Pharmacy · doi:10.1093/ajhp/zxab001 · Cited by 14

Informing the ethical review of human subjects research utilizing artificial intelligence

Makridis, C. A., Boese, A., Fricks, R., Workman, D. E., Klote, M., Mueller, J. M., Hildebrandt, I. J., Kim, M. P., & Alterovitz, G. (2023) · Frontiers in Computer Science · doi:10.3389/fcomp.2023.1235226 · Cited by 16

Artificial Intelligence’s Contribution To Mental Health Education

Anand, N., Pant, L. M., Alam, T., Pundir, S., Thomas, L., & Rakshith, U. R. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104905 · Cited by 13

Development of a practical training method for a healthcare artificial intelligence (AI) chatbot

Lin, J., Joseph, T., Parga-Belinkie, J. J., Mandel, A., Schumacher, R., Neumann, K., Scalise, L., Gaulton, J., Christ, L., Leitner, K., et al. (2021) · BMJ Innovations · doi:10.1136/bmjinnov-2020-000530 · Cited by 13

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease

Portela, A. S. D., Saxena, V., Rosenn, E. H., Wang, S., Masieri, S., Palmieri, J., & Pasinetti, G. M. (2024) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202300605 · Cited by 12

Artificial Intelligence (AI) and Machine Learning (ML) for Healthcare and Health Sciences: The Need for Best Practices Enabling Trust in AI and ML

Aliferis, C., & Simon, G. (2024) · Health informatics · doi:10.1007/978-3-031-39355-6_1 · Cited by 16

A Systemic Review of Large Language Models and Their Implications in Dermatology

Lewandowski, M., Kropidłowska, J., Kvinen, A., & Barańska‐Rybak, W. (2025) · Australasian Journal of Dermatology · doi:10.1111/ajd.14484 · Cited by 14

AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice

Rizzo, M., & Dawson, J. D. (2025) · Annals of Neurology · doi:10.1002/ana.27225 · Cited by 17

Artificial Intelligence and Algorithmic Bias

Williams, N. H. (2023) · The International Library of Bioethics · doi:10.1007/978-3-031-48262-5_1 · Cited by 15

The need for epistemic humility in AI-assisted pain assessment

Katz, R. A., Graham, S. S., & Buchman, D. Z. (2025) · Medicine Health Care and Philosophy · doi:10.1007/s11019-025-10264-9 · Cited by 13

Integrating AI Literacy into Medical Education: Preparing Future Clinicians for an AI-Driven Healthcare System

Chang, L., & Sreedhar, R. (2026) · Medical Science Educator · doi:10.1007/s40670-025-02599-y · Cited by 14

Value-laden challenges for technical standards supporting regulation in the field of AI

Tartaro, A. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09809-y · Cited by 11

Social Cybersecurity and Human Behavior

Muller, S. R., & Burrell, D. N. (2022) · International Journal of Hyperconnectivity and the Internet of Things · doi:10.4018/ijhiot.305228 · Cited by 11

Reliable prediction of childhood obesity using only routinely collected EHRs may be possible

Gupta, M., Eckrich, D., Bunnell, H. T., Phan, T.-L. T., & Beheshti, R. (2024) · Obesity Pillars · doi:10.1016/j.obpill.2024.100128 · Cited by 10

Susquehanna Chorale Spring Concert "Roots and Wings"

Hawkins, D. (2017)

Using DataGrid Control to Realize DataBase of Querying in VB6.0

Hu, S. (2000) · Modern Computer

Study and Two Types of Typical Usage of DataGrid Web Server Control

Hai-tao, L. (2005) · Modern Computer

Achieving Parameter of DBSCAN Based on Datagrid

Xu, Q. (2010)

PACWON: A parallelizing compiler for workstations on a network

Das, D. K., & Das, P. P. (1998) · Journal of Systems Architecture · doi:10.1016/s1383-7621(97)00078-7