Abstract
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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CITATION STYLE
Cardellino, C., Collias, T., Molinet, B., Hain, E., Sun, W., Agerri, R., … Cabrio, E. (2024). ANTIDOTE: ArgumeNtaTIon-Driven explainable artificial intelligence fOr digiTal mEdicine. In Frontiers in Artificial Intelligence and Applications (Vol. 392, pp. 4455–4458). IOS Press BV. https://doi.org/10.3233/FAIA241028
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