Dr. Marzyeh Ghassemi Google Scholar: h-index, Total Citations, and Citation Map
Dr. Marzyeh Ghassemi's h-index is 74 (170 i10-index, 30,282+ total citations across 311+ publications) according to Google Scholar as of October 2026. Dr. Marzyeh Ghassemi is affiliated with EECS/IMES, MIT.
Dr. Marzyeh Ghassemi is a researcher affiliated with EECS/IMES, MIT, specializing in various fields. Their work has been cited 30,282 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Dr. Marzyeh Ghassemi's Citation Metrics
Bibliometric impact based on 311 indexed publications. Of these, 19 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 74
- i10-Index
- 170
- Total Citations
- 30,282
- Citing Countries
- 80
As of October 2026.
Dr. Marzyeh Ghassemi has an h-index of 74 and 30,282 total citations across 311 publications, with research cited by institutions in 80 countries.
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Global Impact Map
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TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
20243,301
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
21 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational framework for responsible healthcare machine learning, subsequently addressing critical algorithmic biases and disease severity prediction to advance equitable clinical AI adoption.
The researcher developed a foundational framework for modeling physiological states in ICUs, subsequently extending this work to clinical intervention prediction and standardized data extraction pipelines.
The researcher established a foundational framework for medical algorithmic auditing, subsequently advancing it into international consensus guidelines for trustworthy AI deployment in healthcare.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Dr. Marzyeh Ghassemi's research
Dr. Marzyeh Ghassemi is a researcher at EECS/IMES, MIT. Their work has been cited 30,282 times across 311 publications (h-index 74), according to Google Scholar.
Their most-cited work, “TRIPOD+ AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods” (2024), has accumulated 3,301 citations. Other influential works include “The false hope of current approaches to explainable artificial intelligence in health care” (2021) with 1,954 citations and “Do no harm: a roadmap for responsible machine learning for health care” (2019) with 1,464 citations.
Citations of Dr. Marzyeh Ghassemi's research come primarily from United States, China and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
