Lessons on the Path from Code to Clinic - Some Common Myths in Machine Learning for Healthcare

Alan Karthikesalingam / Google Health - London

Abstract: Machine learning in healthcare could have transformative impact for patients, caregivers and health systems but the potential benefits remain challenging to realise at scale. Along the path from the development of a model to the realisation of clinical and health-economic impact are a number of challenges and learnings that might be transferable across a range of applications. This talk surveys some recent progress at Google Health and shares learnings from their team in moving from early research to product development; from product development to deployment; and from deployment to early measures of clinical impact.

Bio: Dr. Alan Karthikesalingam is a surgeon-scientist who leads the healthcare machine learning research group at Google Health in London (and formerly for healthcare at DeepMind).

He led DeepMind and Google’s teams in four landmark studies in Nature and Nature Medicine focusing on AI for breast cancer screening with Cancer Research UK, AI for the recognition and prediction of blinding eye diseases with the world’s largest eye hospital (Moorfields) and medical records research with the Veterans Affairs developing AI early warning systems for common causes of patient deterioration, like acute kidney injury.

He is leading work on how machine learning approaches can best promote AI safety as the team takes forward its early research into products for clinical care. Alan continues to practice clinically and supervise PhD students as a lecturer in the vascular surgery department of Imperial College, London.

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