Course summary

Artificial intelligence has immense potential to tackle the major global health challenges, optimise healthcare systems and improve patient outcomes. The greatest challenge to realising this potential is its translation into real-world use. Our programme aims to address this challenge by training interdisciplinary researchers who would possess the technical skills, biomedical domain knowledge, and experience developing and implementing innovative AI approaches in the private and public sectors. Our programme is especially suitable for those with relatively little prior exposure to computer science and mathematics. This includes clinicians, allied health professionals, and biological/biomedical scientists. Our graduates will acquire technical skills from computer science, mathematics, and statistics, and domain knowledge from biomedical and clinical sciences. They will know how to practice responsible and open AI research & innovation, adopt the best practice for minimising the risk of bias in AI, and understand the importance of model explainability for clinical use. The research programme is organised into four thematic areas, each with an expert theme leader supported by groups of at least twenty project supervisors from across the University:

• AI for Genomic Medicine

• AI for Biomedical Imaging

• AI for Cellular and Molecular Systems Medicine

• AI for Biomedical & Health Informatics