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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 endeavor to categorize and synthesize these practices, aiming to provide support and guidance for future researchers and healthcare professionals.<\/jats:p>","DOI":"10.1145\/3709367","type":"journal-article","created":{"date-parts":[[2024,12,21]],"date-time":"2024-12-21T08:50:17Z","timestamp":1734771017000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":19,"title":["Explainable Artificial Intelligence for Medical Applications: A Review"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9228-4543","authenticated-orcid":false,"given":"Qiyang","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8010-6897","authenticated-orcid":false,"given":"Alican","family":"Akman","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6478-8699","authenticated-orcid":false,"given":"Bj\u00f6rn W.","family":"Schuller","sequence":"additional","affiliation":[{"name":"Department of Computing, Imperial College London, London, United Kingdom of Great Britain and Northern Ireland"}]}],"member":"320","published-online":{"date-parts":[[2025,2,17]]},"reference":[{"issue":"1","key":"e_1_3_1_2_2","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1038\/s41598-024-53778-7","article-title":"Artificial intelligence framework for heart disease classification from audio signals","volume":"14","author":"Abbas Sidra","year":"2024","unstructured":"Sidra Abbas, Stephen Ojo, Abdullah Al Hejaili, Gabriel Avelino Sampedro, Ahmad Almadhor, Monji Mohamed Zaidi, and Natalia Kryvinska. 2024. 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