This course aims to introduce students to key concepts and applications of AI in health. Specifically, it will equip students with knowledge and skills to critically appraise and plan AI-related health research.

Intended learning outcomes

Upon successful completion of the course, students will be able to:

• Appreciate the range of applications and potential of AI in health
• Understand the key concepts underlying AI in health solutions, including machine learning, deep learning, computer vision, and large language models and gain hands-on experience in coding some example use-cases (NB this will not entail learning to independently code all types of AI models which is beyond the scope of this introductory course).
• Understand how to evaluate AI technologies for health.
• Appreciate key implementation, regulatory, and ethical issues surrounding the use of AI in health.
• Critically appraise AI-related health research.

Session content

Sessions will be split into the following themes:

• Overview of AI/ML in healthcare (lecture)
• Traditional ML methods with a focus on supervised (regression/classification) and unsupervised (clustering, and dimensionality reduction) learning algorithms (lecture and computer practical)
• Modern ML methods with a focus on artificial neural networks, computer vision, and large language models (lecture and computer practical)
• Evaluation of AI/ML in health solutions considering model validation, effectiveness, cost-effectiveness, bias, and monitoring (lecture)
• Ethical, regulatory, and trust issues surrounding the use of AI/ML in health (lecture)
• Critical appraisal of AI/ML-related health research (lecture and practicals applying learning so far to appraise published literature).

Teaching methods/assessments

The course including lectures and practicals (with group discussions) will be delivered in person. Teaching will be conducted synchronously. Participants will also have the opportunity to network with course organisers and other attendees. The course materials, including PowerPoint presentations, extra reading references, and practical code notebooks will be provided electronically. 

Computer requirements

To participate in this course you’ll need a laptop with internet access and a Gmail account. All coding will be done on Google Colab, so no special software or hardware is required.

Accreditation

The Royal College of Physicians has awarded 11 Continuing Professional Development (CPD) credits for this course.