Description: This workshops explores the use of artificial intelligence, especially large language models, for processing healthcare data such as electronic health records. Learners will examine how LLMs can be used to extract structured information from unstructured text, automate documentation, and uncover patterns in complex datasets. The presentation highlights benefits such as improved efficiency and richer insights, while addressing risks including hallucinations, model biases and privacy concerns. Students will study pitfalls unique to healthcare data and gain an understanding of key technical challenges: data standardization, model validation, safety monitoring, and ensuring transparency and privacy in clinically sensitive and resource constrained environments. We will talk about open source packages and open weight models and how they can be used effectively for clinical data.
Teachers: Alper Celik (HPC4Health, Centre for Computational Medicine SickKids)
Level: Introductory, Intermediate
Format: Lecture + Hands-on (first half lecture, second half hands-on)
Certificate: Attendance
Prerequisites: Basic R and BASH