From medical imaging to clinical safety to regulation - a clear-eyed introduction to how AI is really being applied in health, from the people applying it.
Data-enabled trials and real-world evidence are reshaping how treatments get tested and approved - and creating one of the most established career routes in health data.
Apps, wearables and software-as-a-medical-device - how digital tools get built, regulated, evidenced and adopted in real health systems.
How data and computational methods are changing the way new medicines are discovered - from target identification to longevity medicine.
Bias, fairness and representation in health data and AI - so the science benefits every community, not just the ones well-represented in the datasets.
R, Python and reproducible analytical pipelines are transforming NHS analysis from spreadsheets into engineering - and the analysts who make that jump are in demand everywhere.
Using linked health data to understand disease, inequality and long-term conditions across communities - the analytics that public health runs on.
Involvement and engagement are now expected of every serious data study - a discipline in its own right, with its own methods, roles and career.
From Python foundations to distributed computing - the engineering pathway for people who build the tools other pathways depend on.