Version 1
: Received: 22 August 2024 / Approved: 23 August 2024 / Online: 23 August 2024 (08:24:37 CEST)
How to cite:
Mienye, I. D.; Obaido, G.; Jere, N.; Mienye, E.; Aruleba, K.; Emmanuel, I. D.; Ogbuokiri, B. A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges. Preprints2024, 2024081702. https://doi.org/10.20944/preprints202408.1702.v1
Mienye, I. D.; Obaido, G.; Jere, N.; Mienye, E.; Aruleba, K.; Emmanuel, I. D.; Ogbuokiri, B. A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges. Preprints 2024, 2024081702. https://doi.org/10.20944/preprints202408.1702.v1
Mienye, I. D.; Obaido, G.; Jere, N.; Mienye, E.; Aruleba, K.; Emmanuel, I. D.; Ogbuokiri, B. A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges. Preprints2024, 2024081702. https://doi.org/10.20944/preprints202408.1702.v1
APA Style
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. Preprints. https://doi.org/10.20944/preprints202408.1702.v1
Chicago/Turabian Style
Mienye, I. D., Ikiomoye Douglas Emmanuel and Blessing Ogbuokiri. 2024 "A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges" Preprints. https://doi.org/10.20944/preprints202408.1702.v1
Abstract
Explainable AI (XAI) has the potential to transform healthcare by making AI-driven medical decisions more transparent, trustworthy, and ethically compliant. Despite its promise, the healthcare sector faces several challenges, including balancing 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 and providing valuable insights for researchers and practitioners. The insights and recommendations presented aim to guide future research and policy-making, promoting the development of transparent, trustworthy, and effective AI-driven solutions.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
