Towards transparent healthcare: advancing local explanation methods in Explainable Artificial Intelligence

Metta C., Beretta A., Pellungrini R., Rinzivillo S., Giannotti F.

explainable artificial intelligence  Biology (General)  QH301-705.5  T  Technology  Machine learning  Explainable artificial intelligence  Review  machine learning  Artificial intelligence  artificial intelligence 

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.

Source: BIOENGINEERING, vol. 11 (issue 4)

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