Complete AI Training
Sign inGet my AI kit

Your job's AI kit

Get your AI kit

Tell us who you are and what you do. We show you your kit right away and email you the link: skills, prompts, AI agents, MCP servers and courses for your job.

500+ jobs ready, and we make a kit for any other job. No payment needed to look.

Share

Video course · 14 chapters · 37 min · certificate

Data Science for AI in Healthcare: Introduction and Course Overview

An overview of data science for AI in healthcare from the Malone Center: AI's history in medicine, a case study, questions doctors should ask, overfitting, inputs and outputs, optimality criteria, data splits and class frequencies, representativeness, an example diagnostic study and the course structure.

Become a memberAlready a member? Sign in

What you'll learn

  • Explain AI's role in healthcare
  • Ask the right questions about clinical ML models
  • Recognise overfitting and data-split pitfalls
  • Assess dataset representativeness
  • Read a diagnostic accuracy study
  • Understand the course structure

Chapters

14 chapters · 37:09
  1. 1:38 01Intro Members Introduction Course overview.
  2. 9:33 02History Members AI in healthcare Augmenting doctors.
  3. 1:02 03Support Members Funding Who made it possible.
  4. 1:15 04Case Members Case study Skills in practice.
  5. 2:51 05Practice Members Questions for doctors Impact on practice.
  6. 1:26 06Overfitting Members Overfitting Too flexible.
  7. 1:37 07Data Members Input and output What goes in.
  8. 1:54 08Training Members Optimality criterion What's optimised.
  9. 1:30 09Splits Members Data split and class frequencies How to split.
  10. 3:59 10Representativeness Members Representation and ML vs statistics Who is in the data?
  11. 3:29 11Study Members Example study Diagnostic accuracy.
  12. 1:17 12Recap Members Take-home message The challenge.
  13. 3:33 13Structure Members Course structure Modular design.
  14. 2:05 14Impact Members Grant, impact and course details Measuring success.