Chen, Jonathan H.

Toward expert-level medical question answering with large language models

Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S. R., Cole-Lewis, H., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationTopic Modeling

Cited by 1,734

Machine Learning and Prediction in Medicine — Beyond the Peak of Inflated Expectations

Chen, J. H., & Asch, S. M. (2017) · New England Journal of Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 1,171

Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial

Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J. A., Kanjee, Z., Parsons, A. S., Ahuja, N., et al. (2024) · JAMA Network Open

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 724

Deep learning interpretation of echocardiograms

Ghorbani, A., Ouyang, D., Abid, A., He, B., Chen, J. H., Harrington, R. A., Liang, D. H., Ashley, E. A., & Zou, J. Y. (2020) · npj Digital Medicine

Artificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 518

Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine

Savage, T., Nayak, A., Gallo, R., Rangan, E., & Chen, J. H. (2024) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 265

Chatbot vs medical student performance on free-response clinical reasoning examinations.

Strong, E., DiGiammarino, A., Weng, Y., Kumar, A., Hosamani, P., Hom, J., & Chen, J. H. (2023) · JAMA Internal Medicine

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 169

Applications of machine learning in routine laboratory medicine: Current state and future directions

Rabbani, N., Kim, G. Y. E., Suarez, C. J., & Chen, J. H. (2022) · Clinical Biochemistry

Clinical Laboratory Practices and Quality ControlArtificial Intelligence in Healthcare and Education

Cited by 118

How Chatbots and Large Language Model Artificial Intelligence Systems Will Reshape Modern Medicine: Fountain of Creativity or Pandora’s Box?

Li, R., Kumar, A., & Chen, J. H. (2023) · JAMA Internal Medicine

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 104

Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets

Chen, J. H., Alagappan, M., Goldstein, M. K., Asch, S. M., & Altman, R. B. (2017) · International Journal of Medical Informatics

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 101

Large language model uncertainty proxies: discrimination and calibration for medical diagnosis and treatment

Savage, T., Wang, J., Gallo, R., Boukil, A., Patel, V., Safavi-Naini, S. A. A., Soroush, A., & Chen, J. H. (2025) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 92

Clinical Decision Support and Implications for the Clinician Burnout Crisis

Jankovic, I., & Chen, J. H. (2020) · Yearbook of Medical Informatics

Electronic Health Records SystemsHealthcare professionals’ stress and burnout

Cited by 84

Deep learning evaluation of biomarkers from echocardiogram videos

Hughes, J. W., Yuan, N., He, B., Ouyang, J., Ebinger, J., Botting, P., Lee, J., Theurer, J., Tooley, J. E., Nieman, K., et al. (2021) · EBioMedicine

Artificial Intelligence in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 71

MedAgentBench: a virtual EHR environment to benchmark medical LLM agents

Jiang, Y., Black, K. C., Geng, G., Park, D., Zou, J., Ng, A. Y., & Chen, J. H. (2025) · NEJM AI

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 66

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 66

MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records

Fleming, S. L., Lozano, A., Haberkorn, W. J., Jindal, J. A., Reis, E. P., Thapa, R., Blankemeier, L., Genkins, J. Z., Steinberg, E., Nayak, A., et al. (2023) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 65

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study

Ouyang, D., Theurer, J., Stein, N. R., Hughes, J. W., Elias, P., He, B., Yuan, N., Duffy, G., Sandhu, R. K., Ebinger, J., et al. (2024) · The Lancet Digital Health

ECG Monitoring and AnalysisCardiac, Anesthesia and Surgical Outcomes

Cited by 64

Standing on FURM Ground: A Framework for Evaluating Fair, Useful, and Reliable AI Models in Health Care Systems

Callahan, A., McElfresh, D., Banda, J. M., Bunney, G., Char, D., Chen, J., Corbin, C. K., Dash, D., Downing, N. L., Jain, S. S., et al. (2024) · NEJM Catalyst

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 61

Holistic evaluation of large language models for medical tasks with MedHELM

Bedi, S., Cui, H., Fuentes, M., Unell, A., Wornow, M., Banda, J. M., Kotecha, N., Keyes, T., Mai, Y., Oez, M., et al. (2026) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 59

Internal Medicine Resident Computer Usage: An Electronic Audit of an Inpatient Service

Ouyang, D., Chen, J. H., Hom, J., & Chi, J. (2016) · JAMA Internal Medicine

Patient Safety and Medication ErrorsInnovations in Medical Education

Cited by 47

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

Xu, S., Hom, J., Balasubramanian, S., Schroeder, L. F., Najafi, N., Roy, S., & Chen, J. H. (2019) · JAMA Network Open

Healthcare cost, quality, practicesClinical Laboratory Practices and Quality Control

Cited by 40

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study

Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., Cool, J., Kanjee, Z., Parsons, A. S., Ahuja, N., et al. (2024) · medRxiv

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 38

ChatGPT Influence on Medical Decision-Making, Bias, and Equity: A Randomized Study of Clinicians Evaluating Clinical Vignettes

Goh, E., Bunning, B., Khoong, E., Gallo, R., Milstein, A., Centola, D., & Chen, J. H. (2023) · medRxiv

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 37

Decoding Artificial Intelligence to Achieve Diagnostic Excellence: Learning From Experts, Examples, and Experience

Chen, J. H., Dhaliwal, G., & Yang, D. (2022) · JAMA

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 32

User-centred design for machine learning in health care: a case study from care management

Seneviratne, M. G., Li, R. C., Schreier, M., Lopez-Martinez, D., Patel, B. S., Yakubovich, A., Kemp, J. B., Loreaux, E., Gamble, P., El-Khoury, K., et al. (2022) · BMJ Health & Care Informatics

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 23