World's Best Scientists 2026 revealed!
Marzyeh Ghassemi

Marzyeh Ghassemi

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Rising Stars
2025

D-Index & Metrics

Rising Stars

D-Index
54
Citations
11885
World Ranking
214
National Ranking
31

Computer Science

D-Index
51
Citations
13631
World Ranking
5253
National Ranking
2421

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Marzyeh Ghassemi is affiliated with the Massachusetts Institute of Technology (MIT) in the United States. Their research primarily spans the intersection of medicine and computer science, focusing heavily on the application of artificial intelligence (AI) in healthcare contexts. They have made notable contributions to fields such as Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging, Health Information Management, and General Health Professions.

The scientist's work concentrates on several key topics, including:

  • Artificial Intelligence in Healthcare and Education
  • Machine Learning in Healthcare
  • Explainable Artificial Intelligence (XAI)
  • Artificial Intelligence in Healthcare
  • Radiomics and Machine Learning in Medical Imaging
  • Topic Modeling
  • COVID-19 diagnosis using AI

Ghassemi has published extensively, with papers appearing in a variety of influential venues. Frequent publication outlets include:

  • arXiv (Cornell University)
  • The Lancet Digital Health
  • Nature Medicine
  • Canadian Medical Association Journal
  • bioRxiv (Cold Spring Harbor Laboratory)

Some of their recent publications include:

  • The false hope of current approaches to explainable artificial intelligence in health care, 2021, The Lancet Digital Health
  • TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods, 2024, BMJ
  • Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations, 2021, Nature Medicine
  • AI recognition of patient race in medical imaging: a modelling study, 2022, The Lancet Digital Health
  • Do as AI say: susceptibility in deployment of clinical decision-aids, 2021, npj Digital Medicine

The scientist frequently collaborates with a number of researchers, including Haoran Zhang, Melissa D. McCradden, Xiaoxuan Liu, Leo Anthony Celi, and Lauren Oakden-Rayner. These collaborations are reflected in many co-authored publications that contribute to advancing the knowledge and application of AI in medicine.

Best Publications

  • COVID-19 Image Data Collection: Prospective Predictions are the Future

    Joseph Paul Cohen;Paul Morrison;Lan Dao;Karsten Roth

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

    Marzyeh Ghassemi;Luke Oakden-Rayner;Andrew L Beam

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

    Jenna Wiens;Suchi Saria;Mark Sendak;Marzyeh Ghassemi

  • Ethical Machine Learning in Health Care

    Irene Y. Chen;Emma Pierson;Sherri Rose;Shalmali Joshi

  • Do As AI Say: Susceptibility in Deployment of Clinical Decision-Aids

    Susanne Gaube;Harini S Suresh;Martina Raue;Alexander Merritt

  • A Review of Challenges and Opportunities in Machine Learning for Health

    Marzyeh Ghassemi;Tristan Naumann;Peter Schulam;Andrew L Beam

  • Challenges to the Reproducibility of Machine Learning Models in Health Care.

    Andrew L. Beam;Arjun K. Manrai;Marzyeh Ghassemi

  • Unfolding physiological state: mortality modelling in intensive care units

    Marzyeh Ghassemi;Tristan Naumann;Finale Doshi-Velez;Nicole Brimmer

  • The role of machine learning in clinical research: transforming the future of evidence generation

    E. Hope Weissler;E. Hope Weissler;Tristan Naumann;Tomas Andersson;Rajesh Ranganath

  • Predicting COVID-19 Pneumonia Severity on Chest X-ray With Deep Learning

    Joseph Paul Cohen;Lan Dao;Karsten Roth;Paul Morrison

  • A multivariate timeseries modeling approach to severity of illness assessment and forecasting in ICU with sparse, heterogeneous clinical data

    Marzyeh Ghassemi;Marco A. F. Pimentel;Tristan Naumann;Thomas Brennan

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

    Matthew B. A. McDermott;Shirly Wang;Shirly Wang;Nikki Marinsek;Rajesh Ranganath

  • CheXclusion: Fairness gaps in deep chest X-ray classifiers

    Laleh Seyyed-Kalantari;Guanxiong Liu;Matthew B. A. McDermott;Irene Y. Chen

  • A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies : QUADAS-AI

    Viknesh Sounderajah;Hutan Ashrafian;Sherri Rose;Nigam H. Shah

  • Treating health disparities with artificial intelligence.

    Irene Y Chen;Shalmali Joshi;Marzyeh Ghassemi

  • Predicting early psychiatric readmission with natural language processing of narrative discharge summaries

    A Rumshisky;M Ghassemi;T Naumann;P Szolovits

  • Ethical Machine Learning in Healthcare

    Irene Y. Chen;Emma Pierson;Sherri Rose;Shalmali Joshi

  • Using Ambulatory Voice Monitoring to Investigate Common Voice Disorders: Research Update

    Daryush D. Mehta;Daryush D. Mehta;Jarrad H. Van Stan;Jarrad H. Van Stan;Matías Zañartu;Marzyeh Ghassemi

  • Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach.

    Aniruddh Raghu;Matthieu Komorowski;Leo Anthony Celi;Peter Szolovits

  • Hurtful words: quantifying biases in clinical contextual word embeddings

    Haoran Zhang;Amy X. Lu;Mohamed Abdalla;Matthew McDermott

  • Clinically Accurate Chest X-Ray Report Generation.

    Guanxiong Liu;Tzu-Ming Harry Hsu;Matthew B. A. McDermott;Willie Boag

  • SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

    Nathan Ng;Kyunghyun Cho;Marzyeh Ghassemi

  • Deep Reinforcement Learning for Sepsis Treatment

    Aniruddh Raghu;Matthieu Komorowski;Imran Ahmed;Leo A. Celi

  • MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III

    Shirly Wang;Matthew B. A. McDermott;Geeticka Chauhan;Marzyeh Ghassemi

Frequent Co-Authors

Rajesh Ranganath
Rajesh Ranganath New York University
Finale Doshi-Velez
Finale Doshi-Velez Harvard University
Luca Foschini
Luca Foschini University of Bologna
Yoshua Bengio
Yoshua Bengio University of Montreal
Björn Ommer
Björn Ommer Ludwig-Maximilians-Universität München
Quaid Morris
Quaid Morris Memorial Sloan Kettering Cancer Center
Russell Greiner
Russell Greiner University of Alberta
Katherine A. Heller
Katherine A. Heller Google (United States)

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