ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine - Archive ouverte HAL
Communication Dans Un Congrès Année : 2024

ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine

Résumé

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.
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Dates et versions

hal-04673974 , version 1 (20-08-2024)

Identifiants

  • HAL Id : hal-04673974 , version 1

Citer

Cristian Cardellino, Theo Alkibiades Collias, Benjamin Molinet, Erwan Hain, Wei Sun, et al.. ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine. ECAI 2024 Demos Proceedings - 27th European Conference on Artificial Intelligence, Oct 2024, Santiago de Compostela, Spain. ⟨hal-04673974⟩
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