Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
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Computer Science > Computers and Society
arXiv:2606.30665 (cs)
[Submitted on 18 Jun 2026]
Title:Explainable Artificial Intelligence For The Detection and Characterisation of Stage B Heart Failure
Authors:Ahmed M Salih, Emer Brady, Ranjit Arnold, Gaurav Gulsin, Huiyu Zhouyb, Anvesha Singh, Gerry McCanna
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Abstract:Stage B heart failure is characterized by asymptomatic structural or functional cardiac abnormalities. Identifying individuals at this stage is clinically important, as early detection may enable targeted interventions to prevent progression to symptomatic disease. Explainable artificial intelligence (XAI) may support early detection, transparent risk stratification, and selection of clinically actionable interventions. This review examines the use of XAI in detecting and characterizing stage B heart failure. A literature search of Web of Science, Scopus, and PubMed was conducted on 27 March 2026. Studies were included if they applied AI with XAI techniques to stage B heart failure. After screening, 20 studies were included. Data on modalities, outcomes, demographic reporting, and XAI methods were extracted and synthesized. SHAP was the most commonly used method, followed by LIME, saliency maps, and Grad-CAM; however, XAI adoption was inconsistent, with some studies relying on limited or ad hoc interpretability approaches. Notably, none compared explanations across sex or ethnic subgroups, despite evidence of subgroup differences in disease burden. Evaluation of XAI outputs was often insufficient: some studies did not assess explanations, while others relied only on literature-based comparisons, introducing potential bias. These limitations suggest explainability was not systematically validated or leveraged to support robust and fair clinical inference. XAI shows promise for improving transparency in stage B heart failure identification, but current implementations remain limited. Key gaps include limited consideration of sex and ethnicity, absence of subgroup-specific analyses, inconsistent evaluation, and lack of external validation, all of which constrain generalisability and clinical adoption.
| Subjects: | Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.30665 [cs.CY] |
| (or arXiv:2606.30665v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2606.30665
arXiv-issued DOI via DataCite
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