Artificial Intelligence for Medical Students

The workshop will be held Wednesdays 5PM PST/8PM PST on Oct 20, 27, Nov 3, Nov 10 virtually. Sign up for email updates here!

Artifical intelligence and computer-aided anlaysis have been progressing rapidly, with many potential applications to address clinical problems. This course aims to introduce key AI concepts to medical students to be comfortable in engaging with AI in their clinical careers. No technical background is required.


Workshop Slides and Code:

Session 1

Session 1 Slides: Intro to AI, Definitions, Misconceptions

Session 1

Session 1 CoLab Code: Exploratory Data Analysis

Session 2

Session 2 Slides: Data Science and Preprocessing

Session 1

Session 2 CoLab Code: Data Visualization, PCA, and Clustering

Session 3

Session 3 Slides: Machine Learning Models

Session 3

Session 3 CoLab Code: Decision Trees and Cross-Validation

Session 4

Session 4 Slides: Neural Networks and Modern Methods

Session 4

Session 4 CoLab Code: Neural Networks and Convolutional Neural Networks

This course includes:

Tutorials from AI Engineers

5 1-hour workshops introducing core topics and applications to medicine, including categorical ideas (machine learning, classification, computer vision, supervised/unsupervised learning) and methods (kNN, random forests, neural networks, deep learning, convolutional neural networks).

Guided Programming Project

Instructing how to set up a Python AI program to develop decision trees, a common method in journal papers, emphasizing on data processing - a common task requiring expertise of medical students and residents.

Training to Evaluate AI Applications

Providing enough context to students such that they can assess the capabilities and limitations of AI in a specific project.

Interaction with AI Researchers

Providing the opportunity for students to engage with AI experts to answer and evaluate any questions they have related to AI in their clinical career (photo of some participants in the 2019 workshop).