Freeman, Robert

Multi-model assurance analysis showing large language models are highly vulnerable to adversarial hallucination attacks during clinical decision support

Omar, M., Sorin, V., Collins, J. D., Reich, D., Freeman, R., Gavin, N., Charney, A., Stump, L., Bragazzi, N. L., Nadkarni, G. N., et al. (2025) · Communications Medicine

Adversarial Robustness in Machine LearningArtificial Intelligence in Healthcare and Education

Cited by 204

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

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

The association between obesity and peak antibody titer response in COVID‐19 infection

Soffer, S., Glicksberg, B. S., Zimlichman, E., Efros, O., Levin, M. A., Freeman, R., Reich, D. L., & Klang, E. (2021) · Obesity

COVID-19 Clinical Research StudiesSARS-CoV-2 and COVID-19 Research

Cited by 48

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial*

Levin, M. A., Kia, A., Timsina, P., Cheng, F.-Y., Nguyen, K.-A.-N., Kohli-Seth, R., Lin, H.-M., Ouyang, Y., Freeman, R., & Reich, D. L. (2024) · Critical Care Medicine

Healthcare Technology and Patient MonitoringMachine Learning in Healthcare

Cited by 18

Implementing a machine learning screening tool for malnutrition: insights from qualitative research applicable to other machine learning-based clinical decision support systems

Besculides, M., Mazumdar, M., Phlegar, S., Freeman, R., Wilson, S., Joshi, H., Kia, A., & Gorbenko, K. (2023) · JMIR Formative Research

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 12

Assessing retrieval-augmented large language models for medical coding

Klang, E., Tessler, I., Apakama, D. U., Abbott, E., Glicksberg, B. S., Arnold, M., Moses, A., Sakhuja, A., Soroush, A., Charney, A. W., et al. (2025) · NEJM AI

Medical Coding and Health InformationMachine Learning in Healthcare

Cited by 6

Cloud Platforms for Developing Generative AI Solutions: A Scoping Review of Tools and Services

Patel, D., Raut, G., Cheetirala, S. N., Nadkarni, G. N., Freeman, R., Glicksberg, B. S., Klang, E., & Timsina, P. (2024) · arXiv

Cloud Computing and Resource ManagementArtificial Intelligence Applications

Cited by 5