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Ensuring AI explainability in healthcare: problems and possible policy solutions
Abstract
AI promises to address health services’ quality and cost challenges, however, errors and bias in medical devices decisions pose threats to human health and life. This has also led to the lack of trust in AI medical devices among clinicians and patients. The goal of this article is to assess whether AI explainability principle established in numerous ethical AI frameworks can help address these and other challenges posed by AI medical devices. We first define the AI explainability principle, delineate it from the AI transparency principle, and examine which stakeholders in healthcare sector would need AI to be explainable and for what purpose. Second, we analyze whether explainable AI in healthcare is capable of achieving its intended goals. Finally, we examine robust regulatory approval framework as an alternative – and a more suitable – way in addressing challenges caused by black-box AI.
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“Patients, with their highly limited technical and clinical knowledge, are less likely to draw relevant insights from such technical explanation modalities such as activation maps and feature attribution plots [119].”
From Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review (2026) · cites this paper as [119]
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“Transparency and accountability [22], fairness and bias mitigation [165], ethical frameworks and guidelines [167], privacy and confidentiality [168] are some of the key ethical aspects of XAI in healthcare.”
BackgroundA historical perspective of biomedical explainable AI research
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“42 Briefly, XAI algorithms can be divided into two main categories: transparent models that convey some intrinsic degree of interpretability and post hoc explainability techniques.”
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“…o v e rr i d e i n c o rr e c t d e c i s i o n s a n d t h u s a v o i d h a r m , a s w e ll a s h e l p i m p r o v e A I t e c hn o l o g y ” [ 30 ] “ A p r o t o t y p i c a l e x a m p l e o f t h i s i s a [ h e a l t h c a r e m a c h i n e l e a r n i n g ] m o d e l w h i c h e rr o n e…”
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“In recent years, there has been growing interest in using explainable machine learning models, which provide inter-pretable predictions, in the healthcare field [4, 12, 20, 22, 39, 45, 53].”
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“This trajectory was interpreted as particularly critical for the management of chronic diseases and for mitigating pandemic-related care disruptions [28].”
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“[82] define explainability and transparency and as GDPR, HIPAA, and PIPEDA.”
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Şahin, Ö. U., & Akar, S. (2025) · Health Sciences Quarterly · doi:10.26900/hsq.2847 · Cited by 1
“Aranovich and Matulionyte (2023) offered policy recommendations on how regulations should be aligned with XAI systems, arguing that institutional and legal harmonization is as crucial as technical competence [33].”
Elkadi, M., Sabra, I., & Yacoub, A. G. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5368235
Ethical Implications of Artificial Intelligence Bias and Fairness
Shafik, W. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-2597-2.ch002
Artificial Intelligence in Genomic Research and Advanced Healthcare Applications
Channarayappa, & R, S. K. R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/406754
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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
Metadata
- Authors
- Aranovich, T. D. C., & Matulionyte, R.
- Affiliations
- Universidade Federal do Rio Grande do Sul (BR)
- Law School, Macquarie University, Sydney, Australia
- Corresponding author
- Aranovich, Tatiana de Campos
- Venue
- Information & Communications Technology Law
- Publisher
- Informa UK Limited
- Published
- 2022-11-14
- Volume
- 32
- Issue
- 2
- Pages
- 259-275
- ISSN
- 1360-0834 (ISSN-L)
- 1469-8404
- OpenAlex
- W4308998881
- Cited by
- 18
- References
- 0
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 2 · 2023: 2
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information & Communications Technology Law (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- explainable AI, artificial intelligence in healthcare, AI medical devices, AI transparency, algorithmic bias, AI errors, trust in AI, AI ethics framework, regulatory approval, black-box AI, AI policy
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