Nadkarni, Girish N.

Sociodemographic biases in medical decision making by large language models

Omar, M., Soffer, S., Agbareia, R., Bragazzi, N. L., Apakama, D. U., Horowitz, C. R., Charney, A. W., Freeman, R., Kummer, B., Glicksberg, B. S., et al. (2025) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 173

Albuminuria testing in hypertension and diabetes: an individual-participant data meta-analysis in a global consortium

Shin, J.-I., Chang, A. R., Grams, M. E., Coresh, J., Ballew, S. H., Surapaneni, A., Matsushita, K., Bilo, H. J. G., Carrero, J. J., Chodick, G., et al. (2021) · Hypertension

Chronic Kidney Disease and DiabetesBlood Pressure and Hypertension Studies

Cited by 155

Advanced glycation end products dietary restriction effects on bacterial gut microbiota in peritoneal dialysis patients; a randomized open label controlled trial

Yacoub, R., Nugent, M., Cai, W., Nadkarni, G. N., Chaves, L. D., Abyad, S., Honan, A. M., Thomas, S. A., Zheng, W., Valiyaparambil, S. A., et al. (2017) · PLOS ONE

Gut microbiota and healthAdvanced Glycation End Products research

Cited by 139

Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying

Soroush, A., Glicksberg, B. S., Zimlichman, E., Barash, Y., Freeman, R., Charney, A. W., Nadkarni, G. N., & Klang, E. (2024) · NEJM AI

Medical Coding and Health InformationArtificial Intelligence in Healthcare and Education

Cited by 137

Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

Singh, A., Nadkarni, G., Gottesman, O., Ellis, S. B., Bottinger, E. P., & Guttag, J. V. (2015) · Journal of Biomedical Informatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 136

Large language models for generating medical examinations: systematic review

Artsi, Y., Sorin, V., Konen, E., Glicksberg, B. S., Nadkarni, G., & Klang, E. (2024) · BMC Medical Education

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 128

Evaluating and addressing demographic disparities in medical large language models: a systematic review

Omar, M., Sorin, V., Agbareia, R., Apakama, D. U., Soroush, A., Sakhuja, A., Freeman, R., Horowitz, C. R., Richardson, L. D., Nadkarni, G. N., et al. (2025) · International Journal for Equity in Health

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 110

Evaluating the accuracy of a state-of-the-art large language model for prediction of admissions from the emergency room

Glicksberg, B. S., Timsina, P., Patel, D., Sawant, A., Vaid, A., Raut, G., Charney, A. W., Apakama, D., Carr, B. G., Freeman, R., et al. (2024) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 82

Artificial Intelligence in Cardiovascular Care—Part 2: Applications

Jain, S. S., Elias, P., Poterucha, T., Randazzo, M., Lopez Jimenez, F., Khera, R., Perez, M., Ouyang, D., Pirruccello, J., Salerno, M., et al. (2024) · Journal of the American College of Cardiology

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 74

Type 2 diabetes partitioned polygenic scores associate with disease outcomes in 454,193 individuals across 13 cohorts

DiCorpo, D., LeClair, J., Cole, J. B., Sarnowski, C., Ahmadizar, F., Bielak, L. F., Blokstra, A., Bottinger, E. P., Chaker, L., Chen, Y.-D. I., et al. (2022) · Diabetes Care

Genetic Associations and EpidemiologyDiabetes, Cardiovascular Risks, and Lipoproteins

Cited by 66

Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling

Mayourian, J., La Cava, W. G., Vaid, A., Nadkarni, G. N., Ghelani, S. J., Mannix, R., Geva, T., Dionne, A., Alexander, M. E., Duong, S. Q., et al. (2024) · Circulation

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 59

Benchmarking the confidence of large language models in answering clinical questions: Cross-sectional evaluation study

Omar, M., Agbareia, R., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · JMIR Medical Informatics

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 53

Using fine-tuned large language models to parse clinical notes in musculoskeletal pain disorders

Vaid, A., Landi, I., Nadkarni, G., & Nabeel, I. (2023) · The Lancet Digital Health

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 45

Generating credible referenced medical research: A comparative study of openAI's GPT-4 and Google's gemini

Omar, M., Nassar, S., Hijazi, K., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · Computers in Biology and Medicine

Artificial Intelligence in Healthcare and EducationAcademic Writing and Publishing

Cited by 43

Unmasking fracture risk in type 2 diabetes: the association of longitudinal glycemic hemoglobin level and medications

Wang, B., Wang, Z., Poundarik, A. A., Zaki, M. J., Bockman, R. S., Glicksberg, B. S., Nadkarni, G. N., & Vashishth, D. (2022) · The Journal of Clinical Endocrinology & Metabolism

Bone health and osteoporosis researchDiabetes Treatment and Management

Cited by 37

Whole-genome association analyses of sleep-disordered breathing phenotypes in the NHLBI TOPMed program

Cade, B. E., Lee, J., Sofer, T., Wang, H., Zhang, M., Chen, H., Gharib, S. A., Gottlieb, D. J., Guo, X., Lane, J. M., et al. (2021) · Genome Medicine

Obstructive Sleep Apnea ResearchGenetic Associations and Epidemiology

Cited by 33

Extracting social support and social isolation information from clinical psychiatry notes: comparing a rule-based natural language processing system and a large language model

Patra, B. G., Lepow, L. A., Kasi Reddy Jagadeesh Kumar, P., Vekaria, V., Sharma, M. M., Adekkanattu, P., Fennessy, B., Hynes, G., Landi, I., Sanchez-Ruiz, J. A., et al. (2025) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 32

Multi-center retrospective cohort study applying deep learning to electrocardiograms to identify left heart valvular dysfunction

Vaid, A., Argulian, E., Lerakis, S., Beaulieu-Jones, B. K., Krittanawong, C., Klang, E., Lampert, J., Reddy, V. Y., Narula, J., Nadkarni, G. N., et al. (2023) · Communications Medicine

ECG Monitoring and AnalysisMachine Learning in Healthcare

Cited by 30

Retrieval-Augmented Generation: Advancing personalized care and research in oncology

Zarfati, M., Soffer, S., Nadkarni, G. N., & Klang, E. (2025) · European Journal of Cancer

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 20

Applications of artificial intelligence in vasculitides: a systematic review

Omar, M., Agbareia, R., Naffaa, M. E., Watad, A., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2025) · ACR Open Rheumatology

Kawasaki Disease and Coronary ComplicationsVasculitis and related conditions

Cited by 11

Benchmarking the Confidence of Large Language Models in Clinical Questions

Omar, M., Agbareia, R., Glicksberg, B. S., Nadkarni, G. N., & Klang, E. (2024) · medRxiv

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 10

Leveraging artificial intelligence and data science for integration of social determinants of health in emergency medicine: scoping review

Abbott, E. E., Apakama, D., Richardson, L. D., Chan, L., & Nadkarni, G. N. (2024) · JMIR Medical Informatics

Machine Learning in HealthcareEmergency and Acute Care Studies

Cited by 9

“Large language models in radiology reporting—a systematic review of performance, limitations, and clinical implications”

Artsi, Y., Klang, E., Collins, J. D., Glicksberg, B. S., Korfiatis, P., Nadkarni, G. N., & Sorin, V. (2025) · medRxiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 7

Establishing the relationship between brain cellular senescence and brain structure

Lund, A. N., Kopell, B. H., Golestani, N., Vornholt, E., Thompson, R. C., Liharska, L. E., Luo, Y., Cheng, E., Park, Y. J., Fennessy, B., et al. (2026) · Cell

Telomeres, Telomerase, and SenescenceSingle-cell and spatial transcriptomics

Cited by 6