Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review - Université Sorbonne Paris Nord
Chapitre D'ouvrage Année : 2024

Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review

Résumé

Text classification plays an essential role in the medical domain by organizing and categorizing vast amounts of textual data through machine learning (ML) and deep learning (DL). The adoption of Artificial Intelligence (AI) technologies in healthcare has raised concerns about the interpretability of AI models, often perceived as “black boxes.” Explainable AI (XAI) techniques aim to mitigate this issue by elucidating AI model decision-making process. In this paper, we present a scoping review exploring the application of different XAI techniques in medical text classification, identifying two main types: model-specific and model-agnostic methods. Despite some positive feedback from developers, formal evaluations with medical end users of these techniques remain limited. The review highlights the necessity for further research in XAI to enhance trust and transparency in AI-driven decision-making processes in healthcare.
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Dates et versions

hal-04699246 , version 1 (12-01-2025)

Identifiants

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Ibrahim Alaa Eddine Madi, Akram Redjdal, Jacques Bouaud, Brigitte Seroussi. Exploring Explainable AI Techniques for Text Classification in Healthcare: A Scoping Review. Digital Health and Informatics Innovations for Sustainable Health Care Systems, IOS Press, 2024, Studies in Health Technology and Informatics, ⟨10.3233/SHTI240544⟩. ⟨hal-04699246⟩
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