Centre for Doctoral Training in AI-enabled Healthcare


AI-enabled diagnostics or prognostics

Theme leads: Rachel McKendry and Daniel Alexander

Deep learning enables networks to model complex nonlinear relationships and such Artificial Intelligence methods have found application in clinical diagnosis using either parameters typically embedded in an electronic health record (like blood test results) or the images produced during radiographic exams or in digital pathology suites.

This theme will help us create, initiate and deploy academic research projects centred on clinical use cases of direct applicability in the hospitals. Example projects might include the detection of radiology abnormality; characterisation of tissues and tissue abnormality (e.g. cancer staging); or the serial monitoring of disease.