Explainable Artificial Intelligence for Medical Applications: A Review
Qiyang Sun, Alican Akman, Björn W. Schuller
TL;DR
Explainability is essential for safe, trusted AI-assisted medical decision-making, but clinical adoption requires transparent, justified reasoning from AI systems. This review surveys recent XAI work across visual, audio, and multimodal medical data, and presents a unifying framework that combines four taxonomy criteria to categorize 19 XAI techniques. Analyzing over 100 papers from 2018–2024, the authors identify a dominance of perceptual, post-hoc, model-specific methods and highlight challenges in data quality, generalisability, evaluation standards, bias, and the complexity of 3D imaging. The outlook emphasizes modality-specific customization, multimodal integration, formal verification, stronger data partnerships, and user-centered evaluation to realize trustworthy, personalized AI in healthcare.
Abstract
The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT) scans, and magnetic resonance imaging (MRI) diagnoses, conducts pattern recognition and disease prediction based on acoustic data, delivers prognoses on disease types and developmental trends for patients, and employs intelligent health management wearable devices with human-computer interaction technology to name but a few. While these well-established applications have significantly assisted in medical field diagnoses, clinical decision-making, and management, collaboration between the medical and AI sectors faces an urgent challenge: How to substantiate the reliability of decision-making? The underlying issue stems from the conflict between the demand for accountability and result transparency in medical scenarios and the black-box model traits of AI. This article reviews recent research grounded in explainable artificial intelligence (XAI), with an emphasis on medical practices within the visual, audio, and multimodal perspectives. We endeavour to categorise and synthesise these practices, aiming to provide support and guidance for future researchers and healthcare professionals.