AIM Curriculum
ACADEMIC STRUCTURE
Courses:
The AIM track curriculum combines clinical coursework and experiences with courses in statistical, machine learning & artificial intelligence (AI) methods applied to medicine and medical data. Students in this track, entering with significant preparation in computer science, statistics & machine learning, will learn how to generate research and develop new technologies that harness artificial intelligence to improve healthcare, transform the practice of medicine, and support health equity. The list of required courses can be found here.
The cornerstone of the required curriculum is the flagship AI in Medicine I & II sequence, taught by leading AI researchers at DBMI, including Isaac “Zak” Kohane, Arjun Manrai, Pranav Rajpurkar, Kun Hsing-Yu, and Marinka Zitnik. This sequence will give students the knowledge to create AI that cuts across the latest modalities in fields such as computer vision, generative language models, and graph neural networks, incorporating diverse data types to improve clinical decision-making and biomedical research.
In addition to the required curriculum, students will select at least 4 elective courses in consultation with their AIM faculty advisor. Elective courses may be selected within the Department of Biomedical Informatics, elsewhere at HMS, or at other Harvard schools such as the Harvard T.H. Chan School of Public Health (HSPH), the School of Engineering & Applied Sciences (SEAS) or the business school (HBS), in addition to MIT. Examples of such courses are listed here.
Rotations:
Students will rotate in at least two labs during the first year. Before each rotation, students submit the Rotation Registration.
After each rotation, students submit the Rotation Evaluation.
Choosing a lab:
Students choose a lab by March 31st of the second year (G2). After choosing a dissertation advisor, students submit the Dissertation Advisor Declaration.
Student advising:
Teaching requirement:
Each student is expected to serve as an unpaid Teaching Fellow (TF) for one 4-credit course or two 2-credit courses.
Qualifying Exam (PQE):
Students take and pass the Preliminary Qualifying Examination (PQE) between the summer after G1 and June of G2. See PQE Guidelines and PQE Application to begin the process.
Dissertation Advisory Committee (DAC):
At least one member of the AIM faculty will serve on each student's DAC. Please refer to the Dissertation Advisory Committee Guidelines for more information. Each time you meet with your committee, you must submit the Dissertation Advisory Committee Report to the AIM program director.
Courses:
The AIM track curriculum combines clinical coursework and experiences with courses in statistical, machine learning & artificial intelligence (AI) methods applied to medicine and medical data. Students in this track, entering with significant preparation in computer science, statistics & machine learning, will learn how to generate research and develop new technologies that harness artificial intelligence to improve healthcare, transform the practice of medicine, and support health equity. The list of required courses can be found here.
The cornerstone of the required curriculum is the flagship AI in Medicine I & II sequence, taught by leading AI researchers at DBMI, including Isaac “Zak” Kohane, Arjun Manrai, Pranav Rajpurkar, Kun Hsing-Yu, and Marinka Zitnik. This sequence will give students the knowledge to create AI that cuts across the latest modalities in fields such as computer vision, generative language models, and graph neural networks, incorporating diverse data types to improve clinical decision-making and biomedical research.
In addition to the required curriculum, students will select at least 4 elective courses in consultation with their AIM faculty advisor. Elective courses may be selected within the Department of Biomedical Informatics, elsewhere at HMS, or at other Harvard schools such as the Harvard T.H. Chan School of Public Health (HSPH), the School of Engineering & Applied Sciences (SEAS) or the business school (HBS), in addition to MIT. Examples of such courses are listed here.
Rotations:
Students will rotate in at least two labs during the first year. Before each rotation, students submit the Rotation Registration.
After each rotation, students submit the Rotation Evaluation.
Choosing a lab:
Students choose a lab by March 31st of the second year (G2). After choosing a dissertation advisor, students submit the Dissertation Advisor Declaration.
Student advising:
Teaching requirement:
Each student is expected to serve as an unpaid Teaching Fellow (TF) for one 4-credit course or two 2-credit courses.
Qualifying Exam (PQE):
Students take and pass the Preliminary Qualifying Examination (PQE) between the summer after G1 and June of G2. See PQE Guidelines and PQE Application to begin the process.
Dissertation Advisory Committee (DAC):
At least one member of the AIM faculty will serve on each student's DAC. Please refer to the Dissertation Advisory Committee Guidelines for more information. Each time you meet with your committee, you must submit the Dissertation Advisory Committee Report to the AIM program director.