Glicksberg, Benjamin S.

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2021) · Journal of Healthcare Informatics Research

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 1,542

Federated Learning for Healthcare Informatics

Xu, J., Glicksberg, B. S., Su, C., Walker, P., Bian, J., & Wang, F. (2019) · arXiv

Privacy-Preserving Technologies in DataMachine Learning in Healthcare

Cited by 1,538

Artificial intelligence in cardiology

Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 1,202

Federated learning for predicting clinical outcomes in patients with COVID-19

Dayan, I., Roth, H. R., Zhong, A., Harouni, A., Gentili, A., Abidin, A. Z., Liu, A., Costa, A. B., Wood, B. J., Tsai, C.-S., et al. (2021) · Nature Medicine

Machine Learning in HealthcareCOVID-19 diagnosis using AI

Cited by 842

AKI in Hospitalized Patients with COVID-19

Chan, L., Chaudhary, K., Saha, A., Chauhan, K., Vaid, A., Zhao, S., Paranjpe, I., Somani, S., Richter, F., Miotto, R., et al. (2021) · Journal of the American Society of Nephrology

Acute Kidney Injury ResearchCOVID-19 Clinical Research Studies

Cited by 805

Prevalence and Impact of Myocardial Injury in Patients Hospitalized With COVID-19 Infection

Lala, A., Johnson, K. W., Januzzi, J. L., Russak, A. J., Paranjpe, I., Richter, F., Zhao, S., Somani, S., Van Vleck, T., Vaid, A., et al. (2020) · Journal of the American College of Cardiology

COVID-19 Clinical Research StudiesTakotsubo Cardiomyopathy and Associated Phenomena

Cited by 791

Machine learning in cardiovascular medicine: are we there yet?

Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018) · Heart

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 555

Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments

Brin, D., Sorin, V., Vaid, A., Soroush, A., Glicksberg, B. S., Charney, A. W., Nadkarni, G., & Klang, E. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 319

Deep learning predicts hip fracture using confounding patient and healthcare variables

Badgeley, M. A., Zech, J. R., Oakden-Rayner, L., Glicksberg, B. S., Liu, M., Gale, W., McConnell, M. V., Percha, B., Snyder, T. M., & Dudley, J. T. (2019) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationHip and Femur Fractures

Cited by 290

Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

Vaid, A., Jaladanki, S. K., Xu, J., Teng, S., Kumar, A., Lee, S., Somani, S., Paranjpe, I., Freitas, J. K. D., Wanyan, T., et al. (2020) · JMIR Medical Informatics

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 253

A foundation model for clinician-centered drug repurposing

Huang, K., Chandak, P., Wang, Q., Havaldar, S., Vaid, A., Leskovec, J., Nadkarni, G. N., Glicksberg, B. S., Gehlenborg, N., & Zitnik, M. (2024) · Nature Medicine

Computational Drug Discovery MethodsMachine Learning in Healthcare

Cited by 249

Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., et al. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 248

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Shameer, K., Badgeley, M. A., Miotto, R., Glicksberg, B. S., Morgan, J. W., & Dudley, J. T. (2017) · Briefings in Bioinformatics

Electronic Health Records SystemsHealthcare Technology and Patient Monitoring

Cited by 198

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

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

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

Experimental and real-world evidence supporting the computational repurposing of bumetanide for APOE4-related Alzheimer’s disease

Taubes, A., Nova, P., Zalocusky, K. A., Kosti, I., Bicak, M., Zilberter, M. Y., Hao, Y., Yoon, S. Y., Oskotsky, T., Pineda, S., et al. (2021) · Nature Aging

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 118

Evaluating the use of large language model in identifying top research questions in gastroenterology

Lahat, A., Shachar, E., Avidan, B., Shatz, Z., Glicksberg, B. S., & Klang, E. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationArtificial Intelligence Applications

Cited by 111

Enabling Precision Cardiology Through Multiscale Biology and Systems Medicine

Johnson, K. W., Shameer, K., Glicksberg, B. S., Readhead, B., Sengupta, P. P., Björkegren, J. L. M., Kovacic, J. C., & Dudley, J. T. (2017) · JACC: Basic to Translational Science

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Cited by 83

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

High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data

Zang, C., Zhang, H., Xu, J., Zhang, H., Fouladvand, S., Havaldar, S., Cheng, F., Chen, K., Chen, Y., Glicksberg, B. S., et al. (2023) · Nature Communications

Advanced Causal Inference TechniquesAlzheimer's disease research and treatments

Cited by 69

Utilizing natural language processing and large language models in the diagnosis and prediction of infectious diseases: A systematic review

Omar, M., Brin, D., Glicksberg, B., & Klang, E. (2024) · American Journal of Infection Control

Artificial Intelligence in Healthcare and EducationMachine Learning 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

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer’s Disease

Paranjpe, M. D., Belonwu, S., Wang, J. K., Oskotsky, T., Gupta, A., Taubes, A., Zalocusky, K. A., Paranjpe, I., Glicksberg, B. S., Huang, Y., et al. (2021) · Frontiers in Aging Neuroscience

Alzheimer's disease research and treatmentsNeuroinflammation and Neurodegeneration Mechanisms

Cited by 49