Training & Curriculum
The curriculum of the PhD in Health AI emphasizes an active learning approach that will be used to teach six required courses, including AI, ethical AI, machine learning, natural language processing, clinical applications of AI and biomedical informatics. Students will gain healthcare experience through clinical rotations, clinical collaborations and access to clinical data from the electronic health record.
Cedars-Sinai's PhD in Health AI is pending WSCUC accreditation.
Program Overview
This program will provide doctoral students with the knowledge and practical experience to develop, evaluate and apply cutting-edge AI algorithms and methods for improving patient care. We utilize a hands-on, active learning approach to teaching that reinforces AI concepts through short projects completed during class. Students will be exposed to hospital rotations to better understand how AI might be used in healthcare. Students will work with clinical collaborators and will have access to data from the electronic health record. Graduates of the program will be positioned to improve healthcare and patient outcomes through the rigorous development and deployment of AI algorithms and software.
Curriculum
- Computational Biomedicine: This course will provide a broad introduction to the field of computational biomedicine including the analysis, interpretation, and use of biomedical and clinical data for improving patient care.
- Artificial Intelligence: This course will cover foundational concepts in artificial intelligence, including history, logic, semantics, knowledge engineering, rule-based learning, probability, search and machine learning.
- Ethical Artificial Intelligence: This course will introduce and discuss the ethical issues associated with the application of artificial intelligence methods to clinical data and the deployment of artificial intelligence in the clinic for patient care.
- Machine Learning: This course will cover key concepts and methods in machine learning, including feature selection, feature engineering, model selection, prediction, evaluation and interpretation.
- Natural Language Processing: This course will introduce algorithms, methods, and software for the processing of text and language.
- Clinical Artificial Intelligence: This course will introduce and discuss the challenges and opportunities of using artificial intelligence for patient care with practical examples and use cases.
- Electives: Two electives will be selected by the student to provide a specialization or focus. Examples include Biostatistics, Computational Biology, and Image Analysis.
Clinical Rotations
All students are required to fulfill a minimum of 20 hours of clinical rotations across one or more specialties. During these rotations, students will shadow doctors during patient encounters and observe interactions, utilizing electronic health records and decision-support tools.
Research Rotations
All students will complete three rotations during the first year in candidate dissertation research labs. This process will culminate in identifying a willing research mentor to supervise a dissertation research project.
Dissertation Research
Students are expected to conduct a dissertation research project that generates new knowledge at the intersection of AI and healthcare. The project will facilitate collaboration between AI experts and clinicians, culminating in several peer-reviewed publications.
Have Questions or Need Help?
If you have questions or wish to learn more about the PhD program in Health AI, call us or send a message.