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A Survey of the Application of Explainable Artificial Intelligence in Biomedical Informatics
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Farinaz Tanhaei ,
Fabio Caraffini ,
Benjamin Mora
Applied Sciences, Volume: 15, Issue: 24, Start page: 12934
Swansea University Authors:
Hassan Eshkiki , Fabio Caraffini , Benjamin Mora
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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...
| Published in: | Applied Sciences |
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| ISSN: | 2076-3417 |
| Published: |
MDPI AG
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa71126 |
| 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 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. |
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| Keywords: |
SHAP; LIME; AI; Explainable Artificial Intelligence; XAI; medical imaging; model interpretability; human-centred AI; biomedical informatics; post hoc explanations |
| College: |
Faculty of Science and Engineering |
| Funders: |
This research received no external funding. |
| Issue: |
24 |
| Start Page: |
12934 |