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Belaid, M.K., Bornemann, R., Rabus, M., Krestel, R. and Hüllermeier, E. (2023) Compare-xAI: Toward Unifying Functional Testing Methods for Post-Hoc XAI Algorithms into a Multi-Dimensional Benchmark. In: Longo, L., Ed., Communications in Computer and Information Science, Springer, 88-109.
https://doi.org/10.1007/978-3-031-44067-0_5

has been cited by the following article:

• TITLE: Opportunities and Challenges of Explainable AI (XAI) in Health Care: A Review

  AUTHORS: Mahbuba Begum, Jannatul Ferdush

  KEYWORDS: Artificial Intelligence, Trustworthy, Explainability, Healthcare

  JOURNAL NAME: Journal of Computer and Communications, Vol.14 No.6, June 24, 2026

  ABSTRACT: The use of Artificial Intelligence (AI) as a decision-making model in healthcare applications faces difficulties because of the black box nature of deep learning (DL) models. Doctors must evaluate a patient’s condition based on logical explanations. Therefore, explainable Artificial Intelligence (XAI) plays an important role by providing interpretable explanations for model decisions. This review examines the role of XAI in healthcare by highlighting key techniques, XAI opportunities, application, critical challenges, and recent developments. Also, this research proposes a XAI framework for healthcare system based on existing challenges. Finally, the review outlines practical strategies and future research directions to mitigate these challenges for enhancing trust and usability of XAI systems in real-world healthcare environments.