Understand the key technical concepts of modern machine learning, including supervised learning, self-supervised learning, and generative modeling.
Explore how AI is applied to assist with diagnosis, clinical workflows, and biomedical research.
Learn about the pre-market evaluation and post-market surveillance strategies used to evaluate AI models.
Understand the different regulatory frameworks for AI in medicine.
AI is reshaping health care, offering unprecedented opportunities to improve patient outcomes, enhance clinical efficiency, and drive innovation in research. With the rapid adoption of AI in health care and research, it’s now more important than ever to understand the foundations and applications of this exciting field.
This HMX short course enables you to learn directly from leading Harvard Medical School faculty about the technical foundations of AI, including supervised and self-supervised learning and generative modeling. You will gain insights into various AI pipelines, and how they can be applied to improve patient diagnosis, clinical workflows, and different types of research, as well as the current regulatory landscape of both traditional machine learning models and generative AI.
