A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges

Mienye, Ibomoiye Domor; Obaido, George; Jere, Nobert; Mienye, Ebikella; Aruleba, Kehinde; Emmanuel, Ikiomoye Douglas; Ogbuokiri, Blessing

Abstract

Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, reliable, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including the need to balance interpretability and accuracy, integrating XAI into clinical workflows, and ensuring adherence to rigorous regulatory standards. This paper provides a comprehensive review of XAI in healthcare, covering techniques, challenges, opportunities, and advancements, thereby enhancing the understanding and practical application of XAI in healthcare. The study also explores responsible AI in healthcare, discussing new perspectives and emerging trends, offering valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, fostering the development of transparent, trustworthy, and effective AI-driven solutions.

Citation

Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024). A survey of explainable artificial intelligence in healthcare: Concepts, applications, and challenges. Informatics in Medicine Unlocked, 51, Article 101587. https://doi.org/10.1016/j.imu.2024.101587

• Journal Article Type: Article
• Acceptance Date: Oct 10, 2024
• Online Publication Date: Oct 16, 2024
• Publication Date: Oct 18, 2024
• Deposit Date: Oct 29, 2024
• Publicly Available Date: Oct 29, 2024
• Journal: Informatics in Medicine Unlocked
• Print ISSN: 2352-9148
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 51
• Article Number: 101587
• DOI: https://doi.org/10.1016/j.imu.2024.101587