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Explainable artificial intelligence for smart and ethical healthcare

Sergey M. Avdoshin1 ,  Elena Yu. Pesotskaya2*
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1 School of Computer Engineering, HSE Tikhonov Moscow Institute of Electronics and Mathematics, National Research University Higher School of Economics, Moscow, Russia
2 School of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics, Moscow, Russia
ASH, 025520003 https://doi.org/10.36922/ASH025520003
Received: 26 December 2025 | Revised: 27 January 2026 | Accepted: 2 February 2026 | Published online: 24 February 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

SmartHealth technologies are evolving rapidly, and the emerging Medicine 5.0 paradigm highlights the need for artificial intelligence that pairs high performance with explainability, transparency, and ethical soundness. However, many neural- network approaches remain “black boxes,” limiting their uptake in clinical practice, where justification and trust are essential. This article reviews their applications in diagnosis, monitoring of chronic conditions, and clinical decision support, with particular attention to semantic and ontological interoperability, user-centered explanations, and the ethics of personalization. We pair a critical review with a proposed hybrid framework for trustworthy explainable artificial intelligence in healthcare that integrates neural representations with logical rules, delivering role- adaptive, interactive explanations for clinicians and patients.

Graphical abstract
Keywords
Explainable artificial intelligence
Neuro-symbolic artificial intelligence
Explainability
SmartHealth
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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