Towards Transparent Healthcare: Advancing Local Explanation Methods in Explainable Artificial Intelligence

Author:

Metta Carlo1ORCID,Beretta Andrea1,Pellungrini Roberto2,Rinzivillo Salvatore1ORCID,Giannotti Fosca2

Affiliation:

1. Institute of Information Science and Technologies (ISTI-CNR), Via Moruzzi 1, 56127 Pisa, Italy

2. Faculty of Sciences, Scuola Normale Superiore, P.za dei Cavalieri 7, 56126 Pisa, Italy

Abstract

This paper focuses on the use of local Explainable Artificial Intelligence (XAI) methods, particularly the Local Rule-Based Explanations (LORE) technique, within healthcare and medical settings. It emphasizes the critical role of interpretability and transparency in AI systems for diagnosing diseases, predicting patient outcomes, and creating personalized treatment plans. While acknowledging the complexities and inherent trade-offs between interpretability and model performance, our work underscores the significance of local XAI methods in enhancing decision-making processes in healthcare. By providing granular, case-specific insights, local XAI methods like LORE enhance physicians’ and patients’ understanding of machine learning models and their outcome. Our paper reviews significant contributions to local XAI in healthcare, highlighting its potential to improve clinical decision making, ensure fairness, and comply with regulatory standards.

Funder

European Community

NextGenerationEU

FAIR

SoBigData.it—Strengthening the Italian RI for Social Mining and Big Data Analytics

Publisher

MDPI AG

Reference53 articles.

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