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Rapport Année : 2022

The role of artificial intelligence within in silico medicine

Résumé

The past decade has produced overwhelming evidence that changes in the health status of individuals, measured by well-defined quantitative clinical endpoints, can, in many specific cases, be predicted by computer models (also known as predictive models). This has opened the door to several applications for these computer modelling & simulation technologies, which are generically referred to as in silico medicine. There are different ways to build computer models, depending on the technologies as well as the quality and strength of data and knowledge that are used. When models are built using available scientific knowledge in biophysics, biochemistry, and physiology of the human body, both in healthy and diseased states, we refer to them as knowledge-driven models. When they are developed directly from data, without making any causal assumption such as is the case for artificial intelligence (AI) methods, we refer to them as data-driven models.
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Dates et versions

hal-03810543 , version 1 (11-10-2022)

Identifiants

  • HAL Id : hal-03810543 , version 1

Citer

Liesbet Geris, Cécile Rousseau, Jérome Noailly, Payman Afshari, Michaël Auffret, et al.. The role of artificial intelligence within in silico medicine. [Research Report] IFSTTAR - Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux. 2022, 20p. ⟨hal-03810543⟩
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