PORCHER Raphaël

Associate Professor of Biostatistics at Paris Cité University, 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.

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Topic of interest: Machine learning, prediction, personalized medicine, causal inference

raphael.porcher@aphp.fr

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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.

PORCHER Raphaël

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