Program Overview

Introduction to Clinical Artificial Intelligence (EPI 233)

Overview

This course provides an overview of Artificial Intelligence (AI), with a particular emphasis on clinical applications and considerations. Topics covered include use cases for AI in research and clinical care, AI design decisions, considerations for implementing AI tools in clinical practice, evaluation, interpretability, privacy, and fairness/bias.

Objectives

The objectives for this course are for participants to understand:

• Learn the history and terminology of AI, and understand its major branches.
• Develop intuition for how AI algorithms work
• Examine the use cases of AI in healthcare
• Understand the core decision points when developing any AI system
• Understand important considerations when designing an AI tool for clinical implementation
• Design an AI project and communicate via a proposal
• Develop skills to leverage generative AI tools such as Large Language Models to advance research and clinical operations work.

Prerequisites

None

Faculty

Course Co-Directors:

• Leo Liu, MD, Associate Professor, Department of Medicine
• Peter Washington, PhD, Assistant Professor, Medicine (DoC-IT)

Format

Each week, new material is introduced via in-person lecture. A Laboratory session immediately follows, providing students with time to work on problem sets/activities with supervision and assistance from course leaders.

• Lecture: A lecture covering the core course topics. Time: Thursdays, 2:45 PM - 3:45 PM, beginning April 2
• Computer Laboratory: Students will work on problem sets/activities with supervision and assistance from course leaders. Time: Thursday, 3:45 PM - 4:45 PM, beginning April 2

Materials

All course materials and handouts will be posted on the course's online syllabus. Learners will need to request access to several resources 2 weeks prior to the course in preparation for the course, including the UCSF Research Analysis Environment (RAE), the UCSF de-identified Clinical Data Warehouse, UCSF Versa, and possibly MIMIC IV.

Grading

Grades will be based on total points achieved on two course projects (50% each).

• There will be 2 mini class projects, one for each major segment of the course (traditional ML/AI, generative AI). Each is expected to take around 15-20 hours to complete. Assignments will be submitted to course directors for review.
  • Traditional ML/AI project: The goal of this project is twofold: (1) for learners to create a thorough 5-page single space proposal for a clinical AI training, evaluation, and implementation process and (2) to repurpose the Python code from the course labs and/or to leverage ChatGPT and other LLMs to generate code that can predict a health outcome from a clinical data set of their choosing.
  • Generative AI project: The goal of this project is to have learners develop skills in using Large Language models to perform tasks using clinical notes such as: structured data extraction, cohort discovery, outcome labeling, etc.

Certificate of Completion

Students not in full-year TICR Programs who satisfactorily pass all course requirements will, upon request, receive a Certificate of Course Completion. Only UCSF students (defined as individuals enrolled in UCSF degree or certificate programs) will receive academic credit for courses. Official transcripts are available to UCSF students only. A Certificate of Course Completion will be available upon request to individuals who are not UCSF students and satisfactorily pass all course requirements.

TICR Program

We offer a broad array of educational opportunities in the disciplines required for the conduct of human research. These include:

• Master's in Clinical & Epidemiologic Research
• Advanced Training in Clinical Research Certificate
• One-Year Clinical Research Workshop
• Summer Clinical Research Workshop

Program Options

The TICR program provides various educational opportunities for students, including master's degrees, certificates, and workshops, all focused on clinical and epidemiologic research.

Program Details

• Master's in Clinical & Epidemiologic Research: This program is designed to provide students with a comprehensive education in clinical and epidemiologic research.
• Advanced Training in Clinical Research Certificate: This certificate program offers advanced training in clinical research, covering topics such as study design, data analysis, and research ethics.
• One-Year Clinical Research Workshop: This workshop provides students with hands-on experience in clinical research, covering topics such as research design, data collection, and data analysis.
• Summer Clinical Research Workshop: This workshop is a condensed version of the one-year workshop, providing students with an introduction to clinical research and its applications.

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