MSC in Artificial Intelligence for Medicine & Medical Research (SUSA)

This MSC in Artificial Intelligence for Medicine & Medical Research consolidates core disciplines to address a rapidly increasing skill gap in the healthcare and biomedical research sector. AI is already revolutionising medical imaging, digital pathology, drug development and pharmaceutical research. In the era of genomic medicine, AI will transform the way we diagnose and treat diseases, reducing the impact of the healthcare crisis in industrialised countries caused by cancer, obesity and diabetes.  

This course combines teaching in AI and machine learning (ML), precision medicine, systems biology, bioinformatics and computational biology. The programme consists of (i) introductory modules that aim to familiarise students with the basic concepts of biology and medicine through examples and the analysis of relevant data sets and (ii) advanced modules focusing on AI/ML applications in omics/image analysis and include project work.

What will I learn?

On successful completion of the programme students will be able to:

• Demonstrate a comprehensive knowledge and understanding of the current state-of-the-art methods in AI/ML and their possible applications to biological and medical data
• Understand the research questions and possible applications in these fields that can be solved using AI/ML
• Understand the nature and structure of biological and medical data including those produced by omics (transcriptomics, genomics, proteomics) and imaging methods
• Understand the design of biological and medical research projects
• Demonstrate a knowledge and understanding of the ethical and privacy issues associated with the use of medical and biological data
• Apply AI/ML applications that can drive the discovery and development of new and highly innovative biomedical and biotech methods and products
• Demonstrate skills in problem-solving and incorporating critical thinking and decision-making into a variety of clinical, biopharmaceutical, and biological research applications and environments
• Demonstrate the analytical and technical skills required for the analysis and interpretation of different data types in the exploitation of scientific discovery and development in industrial, academic and clinical settings
• Work with data from biological and biomedical databases and e-health information systems
• Incorporate ethical and data governance considerations into the analysis of patient and research data that satisfy concurrent data protection frameworks in the era of GDPR

Modules

Core modules

• Biological Principles and Cellular Organisation
• High Throughput Technologies
• Graduate Orientation
• Precision Oncology
• AI for Personalised Medicine
• AI For Medical Image Analysis

Options

• Internship Research Project 10
• Internship research project 20
• Data Challenges in Medical Imaging AI: From Models to Clinical Insights
• Machine Learning (Blended Delivery)
• Medical Research Design, Regulations & Ethics
• Professional Skills and Career Development
• Clin Info and Decision Support
• Introduction to Biostatistics
• Data Programming with R (blended)
• Data Programming with R (online)
• Data Prog with Python (online)
• Bioinformatics
• Data Science in Python
• AI for Health
• Advanced Machine Learning
• Deep Learning
• Information Visualisation (Blended Delivery)
• Entrepreneurship in Prec. Med
• Drug Discovery and Development I
• Intro to Genetic Epidemiology
• Stat Network Analysis
• AI in Pathology