4 papers
Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content
Lydie Bednarczyk, Jamil Zaghir, Julien Ehrsam +11
Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient safety risks remain limited. Fai…
FRACCO: A gold-standard annotated corpus of oncological entities with ICD-O-3.1 normalisation
Johann Pignat, Milena Vucetic, Christophe Gaudet-Blavignac +8
Developing natural language processing tools for clinical text requires annotated datasets, yet French oncology resources remain scarce. We present FRACCO (FRench Annotated Corpus…
Tell me why: Visual foundation models as self-explainable classifiers
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Visual foundation models (VFMs) have become increasingly popular due to their state-of-the-art performance. However, interpretability remains crucial for critical applications. In…
ProtoS-ViT: Visual foundation models for sparse self-explainable classifications
Hugues Turbé, Mina Bjelogrlic, Gianmarco Mengaldo +1
Prototypical networks aim to build intrinsically explainable models based on the linear summation of concepts. Concepts are coherent entities that we, as humans, can recognize and…