Artificial Intelligence in Healthcare: Principles and Application

The course will focus on applied AI methods for problems in prevention, diagnosis, therapy, aetiology, and prognosis related areas of healthcare. You will be given a practical introduction to common AI approaches, offering you experience in using different AI and machine learning algorithms and concepts (including decision trees, logistic regression, support vector machines, artificial neural nets, ensembles and deep learning) in the context of healthcare.

By the end of this course, you will be able to: 

• Describe the core concepts of AI and its essential terminology and the potential areas of its application in healthcare.
• Demonstrate the ability in understanding of  foundational concepts of how machine learns through 'traditional' machine learning algorithms including probabilistic learning as well as tree-based methods using healthcare use cases.
• Analyse the latest methodological developments in the field of AI and it’s potential in medicine (e.g., clinical natural language processing, generative AI for biomedicine and deep learning for medical imaging).
• Outline the caveats of applying machine learning in health including bias and inequalities embedded in the data and/or induced by AI models

Assesment

Assessment will be in form of an individual presentation related to practical work (60% of assessment). In addition, your ability to perform practical course work will be assessed (40% of the assessment).