
Master of Science - Artificial Intelligence and Digital Health
115 New Cavendish Street, London, United Kingdom
340025
Last updated: 26/11/2024







About Master of Science - Artificial Intelligence and Digital Health
AI is everywhere. If students want to future-proof their career, a solid understanding of AI is required, including in healthcare. The MSc in Artificial Intelligence and Digital Health offers a comprehensive and interdisciplinary approach which will equip students with this knowledge and prepare them for the dynamic field of digital health.
Whether their background is in life sciences or computing, Westminster will provide students 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 students complete their bespoke data-intensive dissertation project in health, they’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 the location in London, a hub of global AI development, students will 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.
Students will 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.
Students will also consider ethical, social, legal implications (ELSI) and equity, diversity, and inclusion (EDI) related to data generation, handling, mining, and results development. They’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 students with a robust foundation for a career in this rapidly evolving field. They’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.
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