Version 1
: Received: 24 September 2024 / Approved: 25 September 2024 / Online: 25 September 2024 (08:59:09 CEST)
How to cite:
Tuan, D. A. Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics. Preprints2024, 2024091974. https://doi.org/10.20944/preprints202409.1974.v1
Tuan, D. A. Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics. Preprints 2024, 2024091974. https://doi.org/10.20944/preprints202409.1974.v1
Tuan, D. A. Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics. Preprints2024, 2024091974. https://doi.org/10.20944/preprints202409.1974.v1
APA Style
Tuan, D. A. (2024). Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics. Preprints. https://doi.org/10.20944/preprints202409.1974.v1
Chicago/Turabian Style
Tuan, D. A. 2024 "Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics" Preprints. https://doi.org/10.20944/preprints202409.1974.v1
Abstract
Explainable AI (XAI) has emerged as a pivotal tool in healthcare diagnostics, offering much-needed transparency and interpretability in complex AI models. XAI techniques, such as SHAP, Grad-CAM, and LIME, enable clinicians to understand AI-driven decisions, fostering greater trust and collaboration between human and machine in clinical settings. This review explores the key benefits of XAI in enhancing diagnostic accuracy, personalizing patient care, and ensuring compliance with regulatory standards. However, despite its advantages, XAI faces significant challenges, including balancing model accuracy with interpretability, scaling for real-time clinical use, and mitigating biases inherent in medical data. Ethical concerns, particularly surrounding fairness and accountability, are also discussed in relation to AI's growing role in healthcare. The review emphasizes the importance of developing hybrid models that combine high accuracy with improved interpretability and suggests that future research should focus on explainable-by-design systems, reducing computational costs, and addressing ethical issues. As AI continues to integrate into healthcare, XAI will play an essential role in ensuring that AI systems are transparent, accountable, and aligned with the ethical standards required in clinical practice.
Keywords
Explainable AI (XAI); Healthcare diagnostics; Grad-CAM; SHAP; LIME; Trust in AI; Ethical AI; Bias mitigation; Regulatory compliance; Personalized care; AI interpretability; Clinical decision-making
Subject
Computer Science and Mathematics, Computer Science
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.
