Ghassemi, Marzyeh

TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods

Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., et al. (2024) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 4,718

The false hope of current approaches to explainable artificial intelligence in health care

Ghassemi, M., Oakden-Rayner, L., & Beam, A. L. (2021) · The Lancet Digital Health

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 1,724

Do no harm: a roadmap for responsible machine learning for health care

Wiens, J., Saria, S., Sendak, M., Ghassemi, M., Liu, V. X., Doshi-Velez, F., Jung, K., Heller, K., Kale, D., Saeed, M., et al. (2019) · Nature Medicine

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 1,266

PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods

Moons, K. G. M., Damen, J. A. A., Kaul, T., Hooft, L., Andaur Navarro, C., Dhiman, P., Beam, A. L., Van Calster, B., Celi, L. A., Denaxas, S., et al. (2025) · BMJ

Meta-analysis and systematic reviewsStatistical Methods in Epidemiology

Cited by 1,069

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations

Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 928

Ethical Machine Learning in Healthcare

Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M. (2021) · Annual Review of Biomedical Data Science

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 645

AI recognition of patient race in medical imaging: a modelling study

Gichoya, J. W., Banerjee, I., Bhimireddy, A. R., Burns, J. L., Celi, L. A., Chen, L.-C., Correa, R., Dullerud, N., Ghassemi, M., Huang, S.-C., et al. (2022) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 611

Do as AI say: susceptibility in deployment of clinical decision-aids

Gaube, S., Suresh, H., Raue, M., Merritt, A., Berkowitz, S. J., Lermer, E., Coughlin, J. F., Guttag, J. V., Colak, E., & Ghassemi, M. (2021) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationClinical Reasoning and Diagnostic Skills

Cited by 543

Can AI Help Reduce Disparities in General Medical and Mental Health Care?

Chen, I. Y., Peter Szolovits, B., & Ghassemi, M. (2019) · AMA Journal of Ethics

Ethics and Social Impacts of AIMachine Learning in Healthcare

Cited by 401

Challenges to the Reproducibility of Machine Learning Models in Health Care

Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020) · JAMA

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 366

The limits of fair medical imaging AI in real-world generalization

Yang, Y., Zhang, H., Gichoya, J. W., Katabi, D., & Ghassemi, M. (2024) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 285

Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data

Xu, X., Yao, B., Dong, Y., Gabriel, S., Yu, H., Hendler, J., Ghassemi, M., Dey, A. K., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Mental Health via WritingTopic Modeling

Cited by 278

CheXclusion: Fairness gaps in deep chest X-ray classifiers

Seyyed-Kalantari, L., Liu, G., McDermott, M., Chen, I. Y., & Ghassemi, M. (2020) · Biocomputing 2021

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 275

Reproducibility in machine learning for health research: Still a ways to go

McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., & Ghassemi, M. (2021) · Science Translational Medicine

Scientific Computing and Data ManagementResearch Data Management Practices

Cited by 273

The medical algorithmic audit

Liu, X., Glocker, B., McCradden, M. M., Ghassemi, M., Denniston, A. K., & Oakden-Rayner, L. (2022) · The Lancet Digital Health

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 266

Treating health disparities with artificial intelligence

Chen, I. Y., Joshi, S., & Ghassemi, M. (2020) · Nature Medicine

Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AI

Cited by 235

Hurtful words: quantifying biases in clinical contextual word embeddings

Zhang, H., Lu, A. X., Abdalla, M., McDermott, M., & Ghassemi, M. (2020) · Proceedings of the ACM Conference on Health, Inference, and Learning

Machine Learning in HealthcareEthics and Social Impacts of AI

Cited by 122

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Kohane, I. S., Aronow, B. J., Avillach, P., Beaulieu-Jones, B. K., Bellazzi, R., Bradford, R. L., Brat, G. A., Cannataro, M., Cimino, J. J., García-Barrio, N., et al. (2021) · Journal of Medical Internet Research

Electronic Health Records SystemsMedical Coding and Health Information

Cited by 113

Equity in essence: a call for operationalising fairness in machine learning for healthcare

Wawira Gichoya, J., McCoy, L. G., Celi, L. A., & Ghassemi, M. (2021) · BMJ Health & Care Informatics

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 104

Large language models in biomedicine and health: current research landscape and future directions

Lu, Z., Peng, Y., Cohen, T., Ghassemi, M., Weng, C., & Tian, S. (2024) · Journal of the American Medical Informatics Association

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 92

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Gaube, S., Suresh, H., Raue, M., Lermer, E., Koch, T. K., Hudecek, M. F. C., Ackery, A. D., Grover, S. C., Coughlin, J. F., Frey, D., et al. (2023) · Scientific Reports

Artificial Intelligence in Healthcare and EducationExplainable Artificial Intelligence (XAI)

Cited by 92

Tackling bias in AI health datasets through the STANDING TOGETHER initiative

Ganapathi, S., Palmer, J., Alderman, J. E., Calvert, M., Espinoza, C., Gath, J., Ghassemi, M., Heller, K., Mckay, F., Karthikesalingam, A., et al. (2022) · Nature Medicine

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 82

MDAgents: an adaptive collaboration of LLMs for medical decision-making

Kim, Y., Park, C., Jeong, H., Chan, Y., Xu, X., McDuff, D., Lee, H., Ghassemi, M., Breazeal, C., & Park, H. (2024) · Advances in Neural Information Processing Systems 37

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 81

Large language model integrations in cancer decision-making: a systematic review and meta-analysis

Hao, Y., Qiu, Z., Holmes, J., Löckenhoff, C. E., Liu, W., Ghassemi, M., & Kalantari, S. (2025) · npj Digital Medicine

Artificial Intelligence in Healthcare and EducationAI in cancer detection

Cited by 67