Predicting Cancer Progression with Artificial Intelligence

by Dr Adnan El Bakri

Prediction in health requires the digital analysis of massive data. In practice, however, medical information is centralised, not shared and not sufficiently structured, which is a barrier to the application of artificial intelligence. Faced with this observation, the objective was to study the interest of e-health in the sharing and structuring of medical data and then to propose a new, decentralised and more suitable technological solution. After a review of the scientific literature, a multicentre case-control study was first conducted using infrared micro-spectroscopy applied to the tumour parts of a retrospective cohort of 100 patients operated on for kidney cancer and followed for 5 years.

This thesis shows that e-health and new digital technologies can enable more efficient sharing and better structuring of health data for predictive medicine.

This work opens the way to a paradigm shift, a revolution in the medical world, with the possibility, among other things, of prospective exploitation of real-life medical data and continuous feeding of the new University Medical Departments (DMU), while putting the patient at the centre of a connected and better coordinated care pathway.

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