Clinical Artificial Intelligence and Machine Learning Institute

Catalog description

Artificial intelligence (AI) and machine learning (ML) technologies are promising tools to improve clinical care, alleviate clinician burnout, and promote health equity and access. But little high-quality evidence exists to support their use in practice to achieve these aims. Furthermore, the current federal regulatory agencies charged with ensuring effectiveness, safety, and equity are still evolving to keep pace with technological developments. Generative AI models, such as large language models (LLMs), and those that generate images and video, also offer considerable opportunities for advances in medicine but with significant uncertainty in the optimal approaches for balancing oversight and innovation. This Institute provides a broad and basic overview to these emerging themes in clinical AI/ML with an introductory emphasis. Students will leave the Institute with i) an understanding of key AI/ML concepts as they are applied in a clinical and health policy domain, ii) a critical lens to evaluate AI/ML systems, iii) basic knowledge of the evolving regulatory environment around clinical AI/ML systems, and iv) a foundation to support ongoing learning and/or pursue further work in implementing AI/ML methods. There is no coding required for this course although additional materials will be provided for those wishing to study relevant approaches in R or Python in parallel outside of the required coursework.