I am a faculty member at The School of Convergence Science Digital Foundry, Imperial's cross-faculty Institute in applied Artificial Intelligence. I also Chair the Faculty of Medicines AI Research Ctte. My work covers Artificial Intelligence in medical diagnosis and learning health systems. I Co-Chair the Medical Research Council AI Strategic Task Group, and am a member of the NIHR MRC Better Methods Better Research oversight group as well as a member of the NIHR Doctoral Fellowship Awards Panel.

I am a leading exponent internationally of the “Learning Health System’ (LHS) concept. Although my initial training in research was in heath technology assessment, real-world (pragmatic) clinical trials and clinical research in Family Medicine, since 2003 I have worked in the area of Clinical Informatics, being appointed to a Chair in Medical Informatics at Imperial in 2015 and elected one of the first 100 founding fellows of the new UK Faculty of Clinical Informatics in 2017 (Now Health and Social Care Faculty of the British Computer Society). I have had wide exposure to European and US clinical informatics through workshops and symposia.

Prior to moving to Imperial I was Wolfson Professor of General Practice at King's College London.

My interests lie at the intersection of health services research (how to deal with patient problems equitably and efficiently), data semantics and clinical meaning, and machine learning based model validation and evaluation (AI). Currently there are two areas of active research:

1. Cancer diagnosis in Primary Care. I have led a number of projects in the area of multi-modal data integration, analytics and model validation with a view to generate 'start of the consultation' differential diagnosis lists to improve the timeliness and accuracy of cancer early detection in primary care. We have just been awarded a £2.5m grant from the NIHR i4i programme (Artifical Intelligence for Cancer Diagnosis in General Practice: AI-DIP) in collaboration with SOAP Health, a US startup with an AI virtual doctor to take an unbiased medical history and provide prompts for diagnosis prior to a clinical encounter and QMUL.

2. Computable Clinical Guidelines and Explainable AI. Completing the LHS cycle with a computational infrastructure for deploying guidelines as decision support linked to the EHR. Initially supported by an EPSRC Global Health Development Project (www.ROAD2H.org), this area is now supported by Imperial College NHS Trust Biomedical Research Centre. The aim is to both trigger individual guideline recommendations and add explainability. We have moved on to use of symboloic logic in the form of Answer Set Programmimg.

Teaching:

MSc: I am deputy academic director of the Diploma/MSc in Digital Health Leadership and lead the module on Actionable analytics.

PhDs: Currently supervising three students. Please contact me if interested in AI in healthcare using heath data with applications in diagnosis.