Curriculum Overview
The PhD in Health AI is a four-year, primarily in-person program that moves students from a strong shared foundation into sustained, lab-based dissertation research. The curriculum is front-loaded in Year 1, when students complete core training in AI, Machine Learning, Ethical AI and Computational Biomedicine, alongside research rotations, clinical rotations and SCALE (Self-Curated Applied Learning Experiences), a structured self-directed component that lets students tailor emerging topics and technologies to their interests. summer research begins early, so students start developing their dissertation trajectory from the first year rather than waiting until later stages of the program.
In Year 2, students have joined a dissertation lab and build further depth through Natural Language Processing and Translational AI, together with electives and additional SCALE experiences. Students are supported by a co-mentoring model in which each student is guided by both an AI mentor and a clinical mentor, with an additional domain mentor included when appropriate, creating an interdisciplinary mentoring team that supports research design, clinical relevance and professional growth. Across Years 2 through 4, students receive training and close mentorship in proposal development, scientific writing and publishing, with the goal of helping them progress toward dissertation milestones while producing conference papers and high-impact journal publications.
In Years 3 and 4, the program is primarily research-focused, with students devoting most of their time to dissertation work while continuing to learn through optional electives, SCALE activities and ongoing engagement with clinical collaborators. This structure is designed to support timely degree completion while preparing graduates to become independent scientists and leaders at the intersection of AI and healthcare.
Curriculum Information
Course Catalog
Course Descriptions
Translational Artificial Intelligence
This course provides a comprehensive exploration of the intersection between artificial intelligence and biomedical sciences, aimed at equipping AI (Artificial Intelligence) and computer science professionals with the requisite clinical knowledge to develop and apply AI algorithms in healthcare. Students will delve into the principles of clinical medicine, examine case studies of AI applications in clinical settings, and engage in the development of AI solutions to address medical challenges. Key topics include feature engineering, data preprocessing, dimensionality reduction, explainable AI, and setting up appropriate evaluation methods for domain‑specific problems. The course will also address the ethical, regulatory, and practical considerations of implementing AI in healthcare, including dealing with bias and fairness, preparing students to contribute to the advancement of AI-driven clinical and translational research.
Imaging Artificial Intelligence
Imaging AI seeks to advance innovative diagnostic and prognostic algorithms in Radiology and Pathology, equipping you with the competencies to develop and validate AI / deep learning workflows for biomedical image analysis and translate theoretical knowledge into clinical solutions. Through hands‑on learning, you will master AI-driven image analysis for disease biomarker identification, diagnostic and prognostic modeling, and progression tracking and monitoring. The course will also emphasize appropriate statistical validation (e.g., multilevel regression modeling) and evaluation of the AI models. Special topics include graph‑based methods, spatial multimodal analysis, and user interface design.
Devices and Wearables
Designing and inventing new biomedical devices and wearables in any area of healthcare requires a comprehensive clinical and physics‑based understanding of the human body integrated with the art of engineering design. This course focuses on developing devices and wearables for the neuromuscular system. We will start with a brief introduction to the human anatomy, the neuromuscular system, and the behavior of different types of signals, such as electrical, acoustic, and optical waves, that can be used to understand human tissue condition and behavior, along with examples of the current state of the art. We will then delve deep into 2 to 3 clinical problems medical providers face in musculoskeletal medicine, where biomedical devices could improve screening, diagnosis, or assessment and, therefore, improve clinical care. We will focus on pathophysiology, the clinical workflow, and constraints inherent in human subject studies, and the engineering limitations, before exploring potential pathways to develop a biomedical device or wearable. The second part of the class will focus on developing a working prototype of a wearable device. You will gain hands‑on experience with prototyping tools and devices such as high-end 3D printers, Computer-Aided Design (3D design), programming microcontrollers and sensors, and transducers.
Computational Biology
AI algorithms for personalized medicine require multi‑modal data to capture the interactions between our genes and the environment in order to understand disease conditions. This course will cover algorithms and methods used to analyze complex biomedical data, including DNA sequences, genetics, epigenetics, proteomics, single-cell genomics, and molecular image data. A mentored term project will provide you with hands-on experience for carrying out independent research, highlighting the importance of interdisciplinary collaborations and the value of incorporating diverse perspectives in research.
Computational Biomedicine
Computational Biomedicine, a rapidly growing discipline at the intersection of biology, medicine, statistics, and computer science, offers exciting opportunities for real‑world impact in healthcare. In the dynamic landscape of biomedical research, where data plays an increasingly crucial role, understanding scientific inquiries and developing quantitative skills for data analysis and interpretation are essential. This course, serving as an introduction to Computational Biomedicine, will focus on modeling health and disease systems. We will cover computational modeling principles, apply modeling techniques, analyze model performance and limitations, and explore innovative computational frameworks, algorithms, and architectures. These tools are not just theoretical concepts, but practical solutions to address unmet needs and open problems in biomedical research and clinical practice. The course will use project‑based and hands-on learning experiences to enhance students’ understanding and application of the subject matter, preparing them for the exciting challenges of the field.
Ethical Artificial Intelligence
This course explores the ethical challenges and considerations involved in developing and deploying artificial intelligence (AI) systems in healthcare and public health contexts, including responsible use, patient consent, bias of AI algorithms, and fairness in models. You will critically examine predictive models and AI applications used for making important health decisions, addressing factors that lead to trustworthy AI. Through a reverse classroom approach, students will engage in active learning activities to analyze the potential for bias, risk, and social inequity in AI systems. The course will emphasize project‑based learning, allowing students to learn and apply ethical AI principles and practices to real-world healthcare scenarios.
Machine Learning
This course provides comprehensive coverage in machine learning, covering both theoretical foundations and practical applications. Students will learn concepts, algorithms, and techniques used in machine learning. Emphasis will be placed on real‑world applications, particularly in biological and clinical sciences. Students will gain hands-on experience through practical exercises and projects and learn the theory and practice of machine learning from a variety of perspectives. Topics include supervised learning (classification, regression); unsupervised learning (clustering, dimensionality reduction); reinforcement learning; and computational learning theory.
Natural Language Processing
The significant advance of natural language processing (NLP) approaches in the last few years, with the advent of chatbots that seem to hold conversations and even express ‘chain‑of-thought’ reasoning behind their answers, sets the bar high for what these systems can accomplish within the healthcare setting, facilitating patient-physician interaction and improving diagnostic accuracy. This course will take a hands-on approach to explore the boundaries of NLP and Artificial Intelligence, enabling deep understanding of cutting-edge technologies that could help address the hardest problems currently faced by clinicians and patients.
Artificial Intelligence
AI algorithms for personalized medicine require multi‑modal data to capture the interactions between our genes and the environment in order to understand disease conditions. This course will cover algorithms and methods used to analyze complex biomedical data, including DNA sequences, genetics, epigenetics, proteomics, single-cell genomics, and molecular image data. A mentored term project will provide you with hands-on experience for carrying out independent research, highlighting the importance of interdisciplinary collaborations and the value of incorporating diverse perspectives in research.
Rotations
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.
Course Sequence
Frequently Asked Questions
Explore answers to commonly asked questions to help you navigate the program.
You will complete six core courses in AI and computational biomedicine, two electives, two multidisciplinary seminars per trimester and required clinical and research rotations.
Yes. You will select a dissertation lab and develop a thesis project in close collaboration with your primary mentor and co-mentoring team. We are here to guide you every step of the way.
Yes. You will take some required electives to ensure core competencies and choose others to support your specific research interests.
You can complete the program in four years. This timeline includes coursework, clinical and research rotations, dissertation research and support for NIH training grant submissions.
Yes. We designed the curriculum, co-mentoring structure and multidisciplinary seminars to connect AI methods with clinical, biological and population health questions across departments.
You will complete research rotations in your first year. Then you will select a dissertation lab and mentoring team (AI mentor and a clinical or domain co-mentor) aligned with your interests, goals and available projects.
You will receive a stipend and health insurance, along with mentoring in grant writing and structured support to prepare and submit individual NIH training‑grant applications.
Cedars‑Sinai Medical Center is accredited by the WASC Senior College and University Commission (WSCUC).
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PhD in Health Artificial Intelligence
Cedars-Sinai Graduate School of Biomedical Sciences
8687 Melrose Ave.
SuiteG‑700
West Hollywood, CA 90069