Artificial Intelligence and Digital Health

Entry requirements

A minimum of a lower second class honours degree (2:2) in a relevant discipline including health or allied health related subject, or in social care related subjects.

If your first language is not English, you should have an IELTS 6.5 with at least 6.5 in writing and no element below 6.0.

Applicants are required to submit one academic reference.

Months of entry

September

Course content

AI is everywhere. If you want to future-proof your career, a solid understanding of AI is required, including in healthcare. Our MSc in Artificial Intelligence and Digital Health offers a comprehensive and interdisciplinary approach which will equip you with this knowledge and prepare you for the dynamic field of digital health.

Whether your background is in life sciences or computing, we’ll provide you with the necessary tools, methods, and experiences in using AI to analyse complex datasets. These datasets will be drawn from various aspects of healthcare, including diagnosis, disease prevention and wellness promotion. While you complete your bespoke data-intensive dissertation project in health, you’ll have the opportunity to use advanced computational tools and machine learning algorithms to identify novel personalised healthcare interventions.

Benefitting from the UK’s influential role in healthcare data, and our location in London, a hub of global AI development, you’ll learn about datasets, integrating genomic and clinical data from initiatives such as the UK Biobank, Genomics England and Pharm GKB, exploring patterns and hidden associations to inform diagnosis and treatment decisions. With leading companies like Google Deep Mind and the Alan Turing Institute based in London, you’ll be immersed in innovation.

You’ll develop a critical understanding of digital health knowledge bases, algorithms, and emerging trends in AI, whilst acquiring essential skills in data management, visualisation, analysis, and programming languages such as Python and R.

You’ll also consider ethical, social, legal implications (ELSI) and equity, diversity, and inclusion (EDI) related to data generation, handling, mining, and results development. You’ll learn to identify biases, such as those derived from ancestry or sex, in biobanking datasets, and how these biases condition our understanding of the world.

This course will provide you with a robust foundation for a career in this rapidly evolving field. You’ll graduate ready to make meaningful contributions to personalised medicine and address the complex challenges of digital health with ethical considerations and an inclusive global perspective.

Fees and funding