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A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics

Hassan Eshkiki Orcid Logo, Farinaz Tanhaei Orcid Logo, Fabio Caraffini Orcid Logo, Benjamin Mora Orcid Logo

Applied Sciences, Volume: 15, Issue: 24, Start page: 12934

Swansea University Authors: Hassan Eshkiki Orcid Logo, Fabio Caraffini Orcid Logo, Benjamin Mora Orcid Logo

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DOI (Published version): 10.3390/app152412934

Abstract

This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed articles, we examine current trends, as well as th...

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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa71126
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spelling 2026-01-16T15:19:46.0180916 v2 71126 2025-12-09 A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics c9972b26a83de11ffe211070f26fe16b 0000-0001-7795-453X Hassan Eshkiki Hassan Eshkiki true false d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 557f93dfae240600e5bd4398bf203821 0000-0002-2945-3519 Benjamin Mora Benjamin Mora true false 2025-12-09 MACS This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed articles, we examine current trends, as well as the strengths and limitations of methodologies currently used in real-world healthcare settings. Our findings highlight a growing interest in XAI, particularly in medical imaging, yet reveal persistent challenges in clinical adoption, including issues of trust, interpretability, and integration into decision-making workflows. We identify critical gaps in existing approaches and underscore the need for more robust, human-centred, and intrinsically interpretable models, with only 44% of the papers studied proposing human-centred validations. Furthermore, we argue that fairness and accountability, which are key to the acceptance of AI in clinical practice, can be supported by the use of post hoc tools for identifying potential biases but ultimately require the implementation of complementary fairness-aware or causal approaches alongside evaluation frameworks that prioritise clinical relevance and user trust. This review provides a foundation for advancing XAI research on the development of more transparent, equitable, and clinically meaningful AI systems for use in healthcare. Journal Article Applied Sciences 15 24 12934 MDPI AG 2076-3417 SHAP; LIME; AI; Explainable Artificial Intelligence; XAI; medical imaging; model interpretability; human-centred AI; biomedical informatics; post hoc explanations 8 12 2025 2025-12-08 10.3390/app152412934 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Other This research received no external funding. 2026-01-16T15:19:46.0180916 2025-12-09T23:56:10.4600744 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Hassan Eshkiki 0000-0001-7795-453X 1 Farinaz Tanhaei 0009-0000-4159-9863 2 Fabio Caraffini 0000-0001-9199-7368 3 Benjamin Mora 0000-0002-2945-3519 4 71126__36026__1ba31726798c4f15a60485935d1649c3.pdf 71126.VoR.pdf 2026-01-16T15:17:44.5618919 Output 3465512 application/pdf Version of Record true © 2025 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
spellingShingle A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
Hassan Eshkiki
Fabio Caraffini
Benjamin Mora
title_short A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
title_full A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
title_fullStr A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
title_full_unstemmed A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
title_sort A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
author_id_str_mv c9972b26a83de11ffe211070f26fe16b
d0b8d4e63d512d4d67a02a23dd20dfdb
557f93dfae240600e5bd4398bf203821
author_id_fullname_str_mv c9972b26a83de11ffe211070f26fe16b_***_Hassan Eshkiki
d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
557f93dfae240600e5bd4398bf203821_***_Benjamin Mora
author Hassan Eshkiki
Fabio Caraffini
Benjamin Mora
author2 Hassan Eshkiki
Farinaz Tanhaei
Fabio Caraffini
Benjamin Mora
format Journal article
container_title Applied Sciences
container_volume 15
container_issue 24
container_start_page 12934
publishDate 2025
institution Swansea University
issn 2076-3417
doi_str_mv 10.3390/app152412934
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed articles, we examine current trends, as well as the strengths and limitations of methodologies currently used in real-world healthcare settings. Our findings highlight a growing interest in XAI, particularly in medical imaging, yet reveal persistent challenges in clinical adoption, including issues of trust, interpretability, and integration into decision-making workflows. We identify critical gaps in existing approaches and underscore the need for more robust, human-centred, and intrinsically interpretable models, with only 44% of the papers studied proposing human-centred validations. Furthermore, we argue that fairness and accountability, which are key to the acceptance of AI in clinical practice, can be supported by the use of post hoc tools for identifying potential biases but ultimately require the implementation of complementary fairness-aware or causal approaches alongside evaluation frameworks that prioritise clinical relevance and user trust. This review provides a foundation for advancing XAI research on the development of more transparent, equitable, and clinically meaningful AI systems for use in healthcare.
published_date 2025-12-08T05:34:38Z
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