Associate Professor of Biostatistics at Université de Paris, co-director of the Centre Virchow-Villermé Paris Berlin, and member of the METHODS team of CRESS-UMR1153. Member of the Comité d’Evaluation Ethique / Institutional Review Board of Inserm. Senior Associate Editor for Methods at Clinical Orthopaedics and Related Research, and Associate Editor for Statistics, Artificial Intelligence and Modeling Outcomes at the Journal of Hepatology.
Topics of interest
Machine learning, prediction, personalized medicine, causal inference
Project in Prairie
Raphaël Porcher will work on methods to identify optimal individualized treatment strategies using real-life observational data, as well as principled approaches to evaluate their performance and potential impact. He will participate in the teaching and training of medical students in AI and machine learning, and in interdisciplinary programs on AI and health.
Artificial intelligence represents an unprecedented opportunity for clinical decision support in medicine, and decision upon treatments in particular. Observational data are also an invaluable source of information to learn about treatment efficacy, but the methodological aspects of therapeutic evaluation, and the issues of confounding and bias in particular, should not be overlooked, especially in complex, time-dynamic, settings. They are central for clinical applicability and impact. Bringing together strong methodology, theories on causal inference, artificial intelligence and large-scale real-life data has the potential to improve how patients are treated and ultimately population health.