With artificial intelligence permeating more aspects of healthcare and research, the Cedars-Sinai Graduate School of Biomedical Sciences is launching a Ph.D. program focused on Health AI.

The Los Angeles-based program is designed to equip students with cutting-edge AI algorithms, emphasizing the analysis of clinical data to inform patient care. Through active learning, clinical rotations and collaboration with clinicians, our program ensures graduates are poised to navigate and contribute to the dynamic world of AI in medical research and patient well-being.

The curriculum of the Ph.D. in Health AI program, which is pending accreditation, 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.

The program is led by Graciela Gonzalez-Hernandez, Ph.D., vice chair of research and education in the Department of Computational Biomedicine. “The Cedars-Sinai PhD in Health Artificial Intelligence program is designed around a learner-centered philosophy: you bring your own knowledge, past experiences, education, and ideas – and discover with us how to expand it into exciting new directions,” she said in a statement. “You will have the unique opportunity to work alongside world-renowned experts in AI and healthcare, engaging directly with cutting-edge technologies that are shaping the future of medicine. Our program emphasizes the development of practical skills and real-world problem-solving, ensuring that you are not only prepared to excel in your career but also to lead and innovate.”

This program will provide doctoral students with the knowledge and practical experience to develop, evaluate and apply AI algorithms and methods for improving patient care. 

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, Cedars-Sinai said. 

The curriculum includes: 
• 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.

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.

All students will also 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.

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.

The faculty of the program is composed of experienced educators and leading experts in artificial intelligence, bioinformatics, biomedical informatics, biostatistics, computational biology, epidemiology, machine learning and natural language processing.



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