Explainability and Regulatory Compliance in Healthcare: Bridging the Gap for Ethical XAI Implementation
Abstract
Artificial Intelligence (AI) has made remarkable advances in healthcare improving diagnosis accuracy and streamlining patient care. Yet its implementation can present unique challenges notably explainability requirements and compliance regulations to ensure ethical AI implementation. This chapter delves deep into the role of explainability and regulatory compliance when applied to AI applications within healthcare settings. The chapter begins by outlining interpretability's significance within AI models outlining their capacity for providing clear explanations for their decisions. This raises concern among healthcare professionals, patients, regulators, and others regarding black-box AI systems leading to hesitation or resistance toward their inclusion into clinical practice. Furthermore, transparent AI algorithms must build trust and confidence to increase their potential to augment medical decision making rather than hinder it. This chapter stresses their potential by building trust and confidence through building transparent AI algorithms rather than diminishing them within healthcare applications that might otherwise arise due to lack of explanation in black-box AI systems, which causes hesitation or resistance surrounding their integration into clinical practice. Furthermore, this chapter explores healthcare regulations and policies about AI technologies in healthcare settings. Data privacy, patient confidentiality, bias mitigation and algorithmic accountability are discussed within regulatory compliance; healthcare stakeholders must navigate these complex legal frameworks to maintain patient-centric care while exploiting AI's full potential. Additionally, this chapter examines current explainability techniques and emerging methodologies designed to align AI models with regulatory requirements. “White-box” AI models that enable transparency and traceability are discussed here, as well as interpretable machine learning approaches explicitly tailored for the healthcare domain. This chapter concludes by emphasizing the necessity of collaboration among healthcare experts, AI researchers and policymakers. Interdisciplinary partnership allows for the creation of explainable AI models that comply with ethical principles, human rights protections, and patient safety while harnessing its transformative abilities for healthcare delivery.
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Regulators and policymakers prefer explanations that offer transparent, auditable decision pathways to assess compliance, fairness, and accountability.
“regulators and policymakers prefer explanations that offer transparent, auditable decision pathways to assess compliance, fairness, and accountability [120].”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [120]
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Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review
Ebraheem, M., Toghranegar, J., Bridge2AI-Voice Consortium, Bensoussan, Y., & Templeton, J. M. (2026) · Journal of Medical Internet Research · doi:10.2196/83790
- Authors
- Moorthy, U. M. K., Muthukumaran, A. M. J., Kaliyaperumal, V., Jayakumar, S., & Vijayaraghavan, K. A.
- Affiliations
- SRM Institute of Science and Technology (IN)
- Venue
- Explainable Artificial Intelligence in the Healthcare Industry
- Publisher
- Wiley
- Published
- 2025-03-07
- Pages
- 521-561
- OpenAlex
- W4408384245
- Cited by
- 8
- References
- 29
- Citations by year
- 2026: 4 · 2025: 4
- Type
- other
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, interpretability, black-box models, white-box models, model interpretability, algorithmic accountability, data privacy, patient confidentiality, algorithmic bias, trust in AI, clinical decision support, patient safety, ethical AI
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