| Autor: |
Bousquet C; Sorbonne Université, Inserm, université Paris 13, Laboratoire d'informatique médicale et d'ingénierie des connaissances en e-santé, LIMICS, F-75006 Paris, France., Beltramin D; Service de santé publique et information médicale, CHU de Saint Etienne, France. |
| Jazyk: |
angličtina |
| Zdroj: |
Studies in health technology and informatics [Stud Health Technol Inform] 2022 May 25; Vol. 294, pp. 114-115. |
| DOI: |
10.3233/SHTI220407 |
| Abstrakt: |
In 2022, the Medical Informatics Europe conference created a special topic called "Challenges of trustable AI and added-value on health" which was centered around the theme of eXplainable Artificial Intelligence. Unfortunately, two opposite views remain for biomedical applications of machine learning: accepting to use reliable but opaque models, vs. enforce models to be explainable. In this contribution we discuss these two opposite approaches and illustrate with examples the differences between them. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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