Shah, Nigam H.

Finding missed cases of familial hypercholesterolemia in health systems using machine learning

Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 123

Generative artificial intelligence in medicine

Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 119

A Nationwide Network of Health AI Assurance Laboratories

Shah, N. H., Halamka, J. D., Saria, S., Pencina, M., Tazbaz, T., Tripathi, M., Callahan, A., Hildahl, H., & Anderson, B. (2024) · JAMA

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 114

Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine

Guo, L. L., Pfohl, S. R., Fries, J., Johnson, A. E. W., Posada, J., Aftandilian, C., Shah, N., & Sung, L. (2022) · Scientific Reports

Machine Learning in HealthcareDomain Adaptation and Few-Shot Learning

Cited by 103

Rapid identification of slow healing wounds

Jung, K., Covington, S., Sen, C. K., Januszyk, M., Kirsner, R. S., Gurtner, G. C., & Shah, N. H. (2016) · Wound Repair and Regeneration

Wound Healing and TreatmentsArtificial Intelligence in Healthcare

Cited by 100

Systematic Review of Approaches to Preserve Machine Learning Performance in the Presence of Temporal Dataset Shift in Clinical Medicine

Guo, L. L., Pfohl, S. R., Fries, J., Posada, J., Fleming, S. L., Aftandilian, C., Shah, N., & Sung, L. (2021) · Applied Clinical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 96

HyBrow: a prototype system for computer-aided hypothesis evaluation

Racunas, S. A., Shah, N. H., Albert, I., & Fedoroff, N. V. (2004) · Bioinformatics

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 70

Answering real-world clinical questions using large language model based systems

Low, Y. S., Jackson, M. L., Hyde, R. J., Brown, R. E., Sanghavi, N. M., Baldwin, J. D., Pike, C. W., Muralidharan, J., Hui, G., Alexander, N., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 68

Use of repurposed and adjuvant drugs in hospital patients with covid-19: multinational network cohort study

Prats-Uribe, A., Sena, A. G., Lai, L. Y. H., Ahmed, W.-U.-R., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Carter, W., Casajust, P., et al. (2021) · BMJ

COVID-19 Clinical Research StudiesCOVID-19 and healthcare impacts

Cited by 68

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

Association of Hemoglobin A 1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative

Vashisht, R., Jung, K., Schuler, A., Banda, J. M., Park, R. W., Jin, S., Li, L., Dudley, J. T., Johnson, K. W., Shervey, M. M., et al. (2018) · JAMA Network Open

Diabetes Treatment and ManagementDiabetes Management and Research

Cited by 61

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

Zero-Shot Clinical Trial Patient Matching with LLMs

Wornow, M., Lozano, A., Dash, D., Jindal, J., Mahaffey, K. W., & Shah, N. H. (2025) · NEJM AI

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 59

Zero-Shot Clinical Trial Patient Matching with LLMs

Wornow, M., Lozano, A., Dash, D., Jindal, J., Mahaffey, K. W., & Shah, N. H. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 59

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

Clinfo.ai: An Open-Source Retrieval-Augmented Large Language Model System for Answering Medical Questions using Scientific Literature

Lozano, A., Fleming, S. L., Chiang, C.-C., & Shah, N. (2023) · Biocomputing 2024

Information Retrieval and Search BehaviorTopic Modeling

Cited by 55

Answering real-world clinical questions using large language model, retrieval-augmented generation, and agentic systems

Low, Y. S., Jackson, M. L., Hyde, R. J., Brown, R. E., Sanghavi, N. M., Baldwin, J. D., Pike, C. W., Muralidharan, J., Hui, G., Alexander, N., et al. (2025) · DIGITAL HEALTH

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 51

Performing an Informatics Consult: Methods and Challenges

Schuler, A., Callahan, A., Jung, K., & Shah, N. H. (2018) · Journal of the American College of Radiology

Electronic Health Records SystemsArtificial Intelligence in Healthcare and Education

Cited by 37

Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare

Pfohl, S., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J., & Shah, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AIMachine Learning in Healthcare

Cited by 30

APLUS: A Python library for usefulness simulations of machine learning models in healthcare

Wornow, M., Gyang Ross, E., Callahan, A., & Shah, N. H. (2023) · Journal of Biomedical Informatics

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 26

Using Aggregate Patient Data at the Bedside via an On-Demand Consultation Service

Callahan, A., Gombar, S., Cahan, E. M., Jung, K., Steinberg, E., Polony, V., Morse, K., Tibshirani, R., Hastie, T., Harrington, R., et al. (2021) · NEJM Catalyst

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 24

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

ACE: the Advanced Cohort Engine for searching longitudinal patient records

Callahan, A., Polony, V., Posada, J. D., Banda, J. M., Gombar, S., & Shah, N. H. (2021) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 22