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2019-03-05 - Colloque/Présentation - poster - Anglais - page(s)

Itani Sarah , Lecron Fabian , Fortemps Philippe , "Artificial Intelligence for Medical Diagnosis: Insights into Major Paradigms" in Mardi des Chercheurs, Mons, Belgique, 2019

  • Codes CREF : Intelligence artificielle (DI1180)
  • Unités de recherche UMONS : Management de l'Innovation Technologique (F113), Mathématique et Recherche opérationnelle (F151)
  • Instituts UMONS : Institut de Recherche en Technologies de l’Information et Sciences de l’Informatique (InforTech)
  • Centres UMONS : Centre de recherche interdisciplinaire en Psychophysiologie et Electrophysiologie de la cognition (CIPsE)

Abstract(s) :

(Anglais) In a society open to technological progress, Artificial Intelligence (AI) meets only with lukewarm approval. With its increasing impact in our daily life, many see AI as the promise of a better world, while others see it as a threat of human substitution. That being said, AI achieves better acceptance as regards medical research. In this context, modern machine learning gives grounds for hope of developing diagnosis support systems. To ensure these systems present a concrete clinical applicability, it is crucial to consider the appropriate paradigms of development. We present these paradigms and provide guidelines for their implementation in practice.


Mots-clés :
  • (Anglais) machine learning
  • (Anglais) medicine
  • (Anglais) artificial intelligence