Artificial intelligence techniques are increasingly applied to precision psychiatry, assisting in the estimation of health risks, aiding in diagnosis, recommending treatment planning, and supporting the scientific publication process. This workshop will focus on AI techniques for routinely collected electronic health record data, as well as emerging generative AI methods for more complex modalities such as clinical notes and patient conversations.

In this 5-hour workshop, we will explore 3 core aspects of AI: supervised machine learning for risk prediction (e.g. boosting, SHAP, ensembling, and AutoML), deep learning for complex understanding of patients (e.g. Q&A of clinical notes, facial emotion tracking), and generative AI to support scientific research (e.g. AI-assisted literature reviews, SQL code, and summarization).

Each of these sections will feature high-level overviews of essential techniques to build understanding, combined with hands-on running of pre-written code in small groups to make these concepts tangible. Participants will take home high-quality notebooks in R and Python for use in their own projects (or sharing with collaborators), a set of key articles to further explore each technique, as well as a more concrete understanding of emerging opportunities for AI in precision psychiatry.

Please note additional details, including a breakdown of the workshop structure, information about course instructors, and supplementary workshop materials, will be provided.