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
5254
National Ranking
2422

Marzyeh Ghassemi publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Marzyeh Ghassemi sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 152 publications — 28th percentile

28% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Marzyeh Ghassemi D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Marzyeh Ghassemi sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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
Muhammad Mamdani
Muhammad Mamdani University of Toronto
Finale Doshi-Velez
Finale Doshi-Velez Harvard University
Luca Foschini
Luca Foschini University of Bologna
Russell Greiner
Russell Greiner University of Alberta
Björn Ommer
Björn Ommer Ludwig-Maximilians-Universität München
Yoshua Bengio
Yoshua Bengio University of Montreal
Quaid Morris
Quaid Morris Memorial Sloan Kettering Cancer Center

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