Artificial Intelligence in Medicine (AIM) PhD Track at HMS DBMI

The Artificial Intelligence in Medicine (AIM) PhD track, newly developed by the Department of Biomedical Informatics (DBMI) at Harvard Medical School, will enable future academic, clinical, industry, and government leaders to rapidly transform patient care, improve health equity and outcomes, and accelerate precision medicine by creating new AI technologies that reason across massive-scale biomedical data and knowledge.

Innovative Technologies

Students will learn from and work alongside leading AI researchers, including Isaac “Zak” Kohane, Arjun Manrai, Chirag Patel, Pranav Rajpurkar, Kun-Hsing Yu, Marinka Zitnik, Maha Farhat, and Tianxi Cai. Together, they will build AI tools that cut across the latest modalities in fields such as generative language models, graph neural networks, and computer vision, incorporating diverse data types to improve clinical decision-making and biomedical research.

Interdisciplinary Training

In addition to coursework in state-of-the-art medical AI, students will enrich their understanding of the challenges and opportunities for AI as applied to the clinical care system by performing hospital rotations alongside medical students and other Harvard and MIT PhD trainees, through a collaboration with the Harvard–MIT Program in Health Sciences and Technology.

AIM students will gain an unparalleled understanding and appreciation for how their research will tangibly impact health care and patient well-being. This approach is designed to enhance innovation between fields such as statistics, computer science, bioinformatics, artificial intelligence, epidemiology, and clinical medicine in order to effect the changes urgently needed in healthcare.

Translational Research

An innovative aspect of the AI in Medicine track is its co-mentorship model, in which students will select one methodological mentor and one hospital-based clinical scientist mentor. The goal is to encourage significant interdisciplinary alignment among students, DBMI faculty, and HMS-affiliated clinical scientists, thereby increasing the translational impact of the student’s research.

Recommended Background

The AI in Medicine track will train exceptional, computationally minded students how to solve problems in the context of biomedicine and clinical care. While there are no specific background requirements, the track is, by necessity, quantitatively rigorous. Therefore, successful applicants will show a mastery of fields such as statistics, linear algebra, computer science, and machine learning. Foundational biological or medical knowledge, as well as a demonstrated proficiency in AI, may be beneficial.

Prospective applicants: Please do not contact AIM faculty. If you have questions please email AIM@hms.harvard.edu

Curriculum Overview

Courses

Our curriculum combines real-world clinical experiences with coursework in statistical, machine learning, and AI methods and tools. Students are required to complete four core courses and two clinical rotations:

AI in Medicine I (Fall)
AI in Medicine II (Spring)
Human Pathology (Fall)
Intro to Clinical Medicine I (Clinical Experience)
Intro to Clinical Medicine II (Clinical Experience)
Responsible Conduct of Research

Students will also select at least four electives in consultation with their AIM faculty advisor. Elective courses may be within the Department of Biomedical Informatics, elsewhere at Harvard Medical School, at other Harvard schools such as the Harvard T.H. Chan School of Public Health, the School of Engineering & Applied Sciences, Harvard Business School — or at MIT through our unique collaboration with the Harvard–MIT Program in Health Sciences and Technology.

Rotations

Students will rotate in at least two labs during the first year, one of which should be an AIM faculty member's lab. Students will select their dissertation lab by end of March G2.

Preliminary Qualifying Exam (PQE)

Students will take and pass the Preliminary Qualifying Examination (PQE) before June of the second year. The structure of the PQE is the same as that for the Bioinformatics and Integrative Genomics (BIG) PhD track.

Admissions

Applicants will be evaluated holistically, assessing the person’s accomplishments and potential based on all information provided (transcripts, letters of recommendation, personal statement, etc.). There is no threshold for GPA or GRE scores, no required set of courses, and no specific undergraduate majors. After the initial review, a group of students will be invited for in-person interviews, the expenses for which will be covered in full.

Apply

The AI in Medicine PhD track is part of the Biomedical Informatics (BMI) PhD program in the Division of Medical Sciences at Harvard Medical School. The doctoral degree is conferred by the Harvard Kenneth C. Griffin Graduate School of Arts and Sciences (Harvard Griffin GSAS).

You can find more information on the application process and a link to the application in the BMI apply page. Applications closed on 12/1/23 at 5pm ET.

Prospective applicants: Please do not contact AIM faculty. If you have questions please email AIM@hms.harvard.edu