Reinforcement Learning in Medical Artificial Intelligence for Healthcare Professionals

Reinforcement Learning

 

This module focuses on training artificial intelligence (AI) through trial-and-error feedback loops. It covers the following key areas:

 

Core Concepts:

Learn about agents, rewards, actions, policies, and value functions.

 

Methods:

Explore techniques such as Q-learning, deep Q-networks, and policy gradients.

 

Applications:

Understand how reinforcement learning optimises treatment plans, manages ICU ventilators, performs robotic surgery, and schedules clinical resources.

 

Importance:

Reinforcement learning models can continuously adapt to clinical complexities, enhancing patient outcomes through simulation and feedback.

 

Why It Matters:

Reinforcement Learning is a highly effective type of machine learning, especially when healthcare professionals are involved in AI development. It is widely used in healthcare because it includes a reward system that humans control and supervise.

 

Think of data as the fuel for AI. The reward system is like the steering wheel, guiding the work of humans and AI toward specific goals. The better we understand this relationship, the more we can create innovative, safe, high-quality applications focusing on patient needs.

 

CPD Awarded: 4 CPD, with approximately 4 hours required to complete the module plus exam time.