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. Who this programme is for 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. You will know how to:

• practice responsible and open AI research and innovation

• adopt the best practice for minimising the risk of bias in AI

• understand the importance of model explainability for clinical use

• AI for Genomic Medicine

• AI for Biomedical Imaging

• AI for Cellular and Molecular Systems Medicine

• AI for Biomedical & Health Informatics

• train PhD students to use machine learning approaches to address real-world problems in inter-disciplinary biomedical, clinical, and health research settings

• conduct a broad range of co-designed state-of-the-art PhD research projects with external partners in the public and private sector

• develop the innovation and entrepreneurship skills of our students through bespoke training programmes and spin-out support

• foster ethical, reproducible, and open research practices among our students

• provide experience of conducting research in external organisations through 3-month placement projects

• promote participation in the programme by students from diverse academic and cultural backgrounds