5 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…
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
Jamil Zaghir, Marco Naguib, Mina Bjelogrlic +3
Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. How…