This course is designed to equip you with the skills you need in order to contribute to the use of AI technologies in healthcare, with a focus on the use of AI for clinical tasks. The course starts by looking into the fundamentals of AI. Here you will learn about what is artificial intelligence, the main approaches to build intelligent machines, how machines learn from data and what is the basic concept underpinning the success of deep learning algorithms. The course then looks into the lifecycle of clinical AI technologies. In this part you will gain insights into the fundamental concepts and best practices that drive the creation, assessment, and implementation of clinical AI tools. Throughout the course you will engage with real-world examples of AI technology in clinical care. In particular, you will learn about the challenges, and opportunities of AI tools that aim to automate and/or augment basic clinical care tasks, namely: diagnosis, prognosis, risk assessment, and treatment decisions. The course content will be delivered by a series of staged short lectures intercalated with problem-based learning tutorials, designed to consolidate what you learnt in the lectures and to actively engage you in the learning process. The course adopts a mixed-methods approach. In some tutorials you will use high-level Python instructions to demonstrate how existing tools can be applied to tasks introduced in the lectures. In others you will utilise basic mathematical notation and equations, while in others you will employ qualitative methods to help you reason through key concepts.
