Artificial intelligence in pathology : what kind of decision support ?


25 novembre 2022



Salle Ladrière, Collège Mercier, Place Cardinal Mercier, 14 et en ligne (YouTube)

Séminaire du centre CEFISES, avec Océane Fiant, Université de technologie de Compiègne

Series : Life and Health

Argumentaire :

« Two arguments are frequently put forward to justify the deployment of artificial intelligence in medicine: it is either to relieve the physician of repetitive tasks with little added value, or to provide him or her with simple decision support.

I will present a case illustrating the second perspective. It is a project aiming at building a dataset of breast cancer images, which will later be used to train artificial neural networks to detect tumor components on hematoxylin and eosin-stained whole slide images. The systematic inventory of these components should allow the pathologist to “see” things that he or she cannot detect with the naked eye, thereby improving his or her ability to analyze breast tumors, diagnose them and manage patients.

However, the study of this case reveals a challenge other than that of providing the pathologist with a simple decision support. For some years now, the management of patients according to the characteristics of their tumor has been based on molecular assays that correlate genetic variants with pathological phenotypes. These assays are used in certain clinical cases to choose some therapeutic options over others. While it is possible to argue that these assays do not compete with, but merely complement the pathologist’s expertise, the fact remains that they can guide clinical decisions according to knowledge and criteria that are not part of the epistemic equipment of this practitioner. Thus, by enhancing the latter’s ability to analyze tumors, artificial intelligence tools are also part of a professional strategy to reinforce the pathologist’s expertise, faced with genomics-based approaches.

My presentation aims at examining the design process of this dataset, by comparing its objectives and its implementation to those of available gene expression assays (mainly OncotypeDX). »

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