Singh, Karandeep

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

Vasey, B., Nagendran, M., Campbell, B., Clifton, D. A., Collins, G. S., Denaxas, S., Denniston, A. K., Faes, L., Geerts, B., Ibrahim, M., et al. (2022) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviews

Cited by 1,039

External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients

Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., et al. (2021) · JAMA Internal Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 995

The Clinician and Dataset Shift in Artificial Intelligence

Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 855

A Systematic Review of Testing and Evaluation of Healthcare Applications of Large Language Models (LLMs)

Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., Fries, J. A., Wornow, M., Swaminathan, A., Lehmann, L. S., et al. (2024) · JAMA

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 738

Testing and Evaluation of Health Care Applications of Large Language Models: A Systematic Review

Bedi, S., Liu, Y., Orr-Ewing, L., Dash, D., Koyejo, S., Callahan, A., Fries, J. A., Wornow, M., Swaminathan, A., Lehmann, L. S., et al. (2025) · JAMA

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 662

The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence

Sounderajah, V., Guni, A., Liu, X., Collins, G. S., Karthikesalingam, A., Markar, S. R., Golub, R. M., Denniston, A. K., Shetty, S., Moher, D., et al. (2025) · Nature Medicine

Meta-analysis and systematic reviewsArtificial Intelligence in Healthcare and Education

Cited by 352

Usability of Commercially Available Mobile Applications for Diverse Patients

Sarkar, U., Gourley, G. I., Lyles, C. R., Tieu, L., Clarity, C., Newmark, L., Singh, K., & Bates, D. W. (2016) · Journal of General Internal Medicine

Mobile Health and mHealth ApplicationsUsability and User Interface Design

Cited by 257

Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain

Singh, K., Drouin, K., Newmark, L. P., Lee, J., Faxvaag, A., Rozenblum, R., Pabo, E. A., Landman, A., Klinger, E., & Bates, D. W. (2016) · Health Affairs

Mobile Health and mHealth ApplicationsHealth, Technology, Consumer Behavior

Cited by 201

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

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 195

Evaluation of performance measures in predictive artificial intelligence models to support medical decisions: overview and guidance

Van Calster, B., Collins, G. S., Vickers, A. J., Wynants, L., Kerr, K. F., Barreñada, L., Varoquaux, G., Singh, K., Moons, K. G., Hernandez-Boussard, T., et al. (2025) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationStatistical Methods in Epidemiology

Cited by 112

Evaluating a Widely Implemented Proprietary Deterioration Index Model among Hospitalized Patients with COVID-19

Singh, K., Valley, T. S., Tang, S., Li, B. Y., Kamran, F., Sjoding, M. W., Wiens, J., Otles, E., Donnelly, J. P., Wei, M. Y., et al. (2021) · Annals of the American Thoracic Society

COVID-19 Clinical Research StudiesMachine Learning in Healthcare

Cited by 111

Patients’ and Nephrologists’ Evaluation of Patient-Facing Smartphone Apps for CKD

Singh, K., Diamantidis, C. J., Ramani, S., Bhavsar, N. A., Mara, P., Warner, J., Rodriguez, J., Wang, T., & Wright-Nunes, J. (2019) · Clinical Journal of the American Society of Nephrology

Mobile Health and mHealth ApplicationsHealth, Technology, Consumer Behavior

Cited by 79

Evaluation of sepsis prediction models before onset of treatment

Kamran, F., Tjandra, D., Heiler, A., Virzi, J., Singh, K., King, J. E., Valley, T. S., & Wiens, J. (2024) · NEJM AI

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 74

Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US

Lyons, P. G., Hofford, M. R., Yu, S. C., Michelson, A. P., Payne, P. R. O., Hough, C. L., & Singh, K. (2023) · JAMA Internal Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 62

Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study

Kamran, F., Tang, S., Otles, E., McEvoy, D. S., Saleh, S. N., Gong, J., Li, B. Y., Dutta, S., Liu, X., Medford, R. J., et al. (2022) · BMJ

Machine Learning in HealthcareCOVID-19 Clinical Research Studies

Cited by 60

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

Wong, A., Cao, J., Lyons, P. G., Dutta, S., Major, V. J., Ötleş, E., & Singh, K. (2021) · JAMA Network Open

Sepsis Diagnosis and TreatmentHealthcare Technology and Patient Monitoring

Cited by 57

Predicting Intensive Care Transfers and Other Unforeseen Events: Analytic Model Validation Study and Comparison to Existing Methods

Cummings, B. C., Ansari, S., Motyka, J. R., Wang, G., Medlin Jr, R. P., Kronick, S. L., Singh, K., Park, P. K., Napolitano, L. M., Dickson, R. P., et al. (2021) · JMIR Medical Informatics

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 35

How Academic Medical Centers Govern AI Prediction Tools in the Context of Uncertainty and Evolving Regulation

Nong, P., Hamasha, R., Singh, K., Adler-Milstein, J., & Platt, J. (2024) · NEJM AI

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 34

The Spanish Availability and Readability of Diabetes Apps

Rodriguez, J. A., & Singh, K. (2018) · Journal of Diabetes Science and Technology

Mobile Health and mHealth ApplicationsHealth Literacy and Information Accessibility

Cited by 30

Not All Clinical AI Monitoring Systems Are Created Equal: Review and Recommendations

Feng, J., Xia, F., Singh, K., & Pirracchio, R. (2025) · NEJM AI

Healthcare Technology and Patient MonitoringArtificial Intelligence in Healthcare and Education

Cited by 28

Enabling collaborative governance of medical AI

Price, W. N., Sendak, M., Balu, S., & Singh, K. (2023) · Nature Machine Intelligence

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 27

Challenges in the postmarket surveillance of clinical prediction models.

Ansari, S., Baur, B., Singh, K., & Admon, A. J. (2025) · NEJM AI

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 22