Pioneering Individualized Medicine

Female Medicine through Machine Learning

MIT’s Female Medicine through Machine Learning (FMML) leverages AI and real-world data to pioneer individualized medicine, addressing overlooked gaps in female health and transforming disease discovery, detection, and delivery.

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Our Vision

MIT’s Female Medicine through Machine Learning (FMML) will pioneer personalized medicine for women. It aims to close the women’s healthcare gap by pioneering the development of open-source medicine for females using artificial intelligence and real-world health data sets.

FMML research body

Our Approach

Female data (cells, animals, humans) accounts for only ~5% of the cumulative research body. Women have sex-specific cells, organs and conditions that modulate all other physiological functioning. In addition to yielding female-only diseases, they create differential disease prevalence, diagnostic symptoms, drug responses, and treatment needs for all disease. Incredibly, this remains largely unaddressed in current medicine.

We will leverage two breakthroughs: artificial intelligence (AI) and real-world datasets (RWD) in medicine. AI allows massive, rapid data analysis at scale while real-world datasets (RWD), unlike medical research ones, are 50% female, inexpensive, and ubiquitous.

We will focus on three areas:

AI discovery

Basic research permitting fundamentally novel insight into female biology

AI detection

Creation of disease detection tools for females

AI delivery

Launching tools in healthcare settings to produce outsized impact for females

Our Team

A collaborative team of faculty and PhD students from MIT and other institutions

Former neuroscientist, AI CEO, Visiting Innovation Scholar at Schwarzman College of Computing

Marzyeh Ghassemi

The Germeshausen Career Development Professor and Associate Professor, Department of Electrical Engineering and Computer Science, and Institute for Medical Engineering & Science

School of Engineering Teaching Innovation Professor of Biological and Mechanical Engineering and MacVicar Fellow, where she directs the Center for Gynepathology Research

Sendhil Mullainathan

Peter de Florez Professor, Professor of EECS; Professor of Economics, Dual Appointment [AI+D]

Associate Director of the Pediatric Surgical Research Laboratories, Massachusetts General Hospital; Associate Molecular Biologist, Massachusetts General Hospital; Associate Professor, Harvard Medical School

Jennifer Garrison

Co-Founder and Co-Director of the Center for Healthy Aging in Women; Assistant Professor at the Buck Institute for Research on Aging.

Female Medicine through Machine Learning

Research projects

Creation of a canon of female medicine

Countless researchers have produced outstanding work in women’s health yet due to underfunding, the research remains piecemeal. To maximize this prior work, we will use AI to aggregate the extent literature on sex differences, and open source the results. We have begun a pilot project.

Endometriosis diagnostic

Endometriosis is notoriously difficult to diagnose, with delays averaging 7–10 years due multiple factors. Using AI on multiple RWD, we are developing models that will potentially lead to earlier diagnoses of endometriosis, and minimize downstream adverse outcomes, such as emergency department visits and surgical complications.

Of the 3.6M births per year, 10% are pre-term, conferring higher risk of mortality or significant lifelong complications. Marzyeh Ghassemi is building an AI diagnostic algorithm to predict pre-term birth, in collaboration with Kaiser Permanente.

Ovarian aging biomarker

The ovaries are critical for the overall health of a woman. When ovaries cease functioning properly (menopause, premature ovarian failure), women’s risk of cardiovascular disease, stroke, cognitive decline and other disorders increase significantly. We are using both modifiable and non-modifiable factors to develop a biomarker for ovarian aging, or menopause. In collaboration with Jennifer Garrison, PhD.

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