Published October 19, 2024
| Version v1
Conference paper
ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine
Contributors
Others:
- Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS) ; Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S) ; Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)
- KU Leuven Plant Institute (LPI) ; Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven)
- IXA NLP Group ; University of the Basque Country = Euskal Herriko Unibertsitatea (UPV / EHU)
- ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019)
- ANR-21-CHR4-0002,ANTIDOTE,Argumentation pour l'intelligence artificielle explicable dans le domaine de la médecine numérique(2021)
Description
The need for transparent AI systems in sensitive domains like medicine has become key. In this paper we present ANTIDOTE, a software suite proposing different tools for argumentation-driven explainable Artificial Intelligence for digital medicine. Our system offers the following functionalities: multilingual argumentative analysis for the medical domain, explanation extraction and generation of clinical diagnoses, multilingual large language models for the medical domain, and the first multilingual benchmark for medical question-answering. Experimental results demonstrate the efficacy of ANTIDOTE across different tasks, highlighting its potential as an asset in medical research and practice and fostering transparency, which is crucial for informed decision-making in healthcare.
Abstract
International audienceAdditional details
Identifiers
- URL
- https://hal.science/hal-04673974
- URN
- urn:oai:HAL:hal-04673974v1
Origin repository
- Origin repository
- UNICA