Medical AI Bootcamp
A program for closely mentored research at the intersection of AI and Medicine. Open to students at Harvard & Stanford, and to medical doctors around the world.
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The Medical AI Bootcamp provides Harvard and Stanford students an opportunity to do cutting-edge research at the intersection of AI and medicine. Over 6-9 months, members receive training to work on high-impact research problems in small interdisciplinary teams.

This bootcamp is a joint effort between Harvard and Stanford, and hosted virtually at the Rajpurkar lab.

New: Medical Doctors outside of Harvard and Stanford with an MD or equivalent can also apply to be part of the program.

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What we do

We tackle important problems in medicine that require artificial intelligence solutions. We work on problems across clinical domains including radiology, emergency medicine, and cardiology, and develop computer vision and natural language processing solutions.

What you learn

You tackle a scoped-out research project in a small team from conception to co-authoring a manuscript. You receive mentorship from faculty in team meetings on project execution and direction. You develop knowledge of cutting-edge medical AI research in reading groups, and machine learning tools in skills building sessions.

What we expect

The bootcamp is fast-paced and highly collaborative. As a result, candidates are expected to be able to dedicate between 10-15 hours / week to the bootcamp over the course of the research project (typical time: 6-9 months), attending one large group meeting once a week and two smaller project meetings a week during the afternoons ET time.

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Pranav Rajpurkar, PhD
Assistant Professor
Harvard Medical School

Andrew NG, PhD
Adjunct ProfessoR
Stanford University

Clinical Mentors

David Kim, Md, PhD
‍Assistant Professor
Stanford Medicine

Agustina Saenz MD, MPH
Academic Hospitalist, Brigham and Women's Hospital

Technical Mentors

Alex Tamkin
PhD Student
Stanford University

Henrik Marklund
PhD Student
Stanford University


Fall 2022 Cohort

Aman kansal

Anirudh Sriram

Michelle Qin

Summer 2022 Cohort

Farah Dadabhoy MD MSC

Jonathan Williams

Nathan Chi

Vignav Ramesh

Vishwanatha Rao

Spring 2022 Cohort

Benjamin Chang

FanG Cao

Jaehwan Jeong

Julie Chen

Lauren Chen

Priya Khandelwal

Lucy He

Raja Narayan MD MPh

Schwinn Saereesitthipitak

Sina Hartung

Winter 2022 Cohort

Amy Zhang

Arvind Saligrama

Brian Soetikno, MD PhD

Elaine Liu

KaT Tian

Andrew Li, MD

Sameer Sundrani

Xinyi Wang


We tackle important problems in medicine that require artificial intelligence solutions. You can see some of the projects we have worked on previously in the bootcamp or in its earlier iteration.

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports (Published in NeurIPS Datasets and Benchmarks 2021)
Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning (Published in Nature Biomedical Engineering, 2022)
Benchmarking saliency methods for chest X-ray interpretation (Published in Nature Machine Intelligence, 2022)


AI Specialization: Candidates with practical machine learning knowledge, and ability to code in Python; undergraduates and graduate students are expected to sign up for research credits for one semester or quarter. We actively seek and welcome candidates from historically underrepresented groups, candidates without previous research experiences, and candidates with primary fields of study outside STEM.

Medicine Specialization: The medicine specialization is open to medical doctors that have already received an MD or equivalent. In the upcoming round, we are especially interested in specialists in radiology and ophthalmology.

The medicine specialization welcomes candidates from around the country and world. The AI specialization is currently open for Harvard and Stanford affiliates only.

Submit your application

We conduct rolling interviews and selections. The next phase of applications will be evaluated on:
- Feb 27th
- March 15th
- April 2nd
- May 4th

We will reach out to you if you are selected for the interview round.

Submit Application