ArticleArtificial intelligence review2024
A review of evaluation approaches for explainable AI with applications in cardiology.
Article in Artificial intelligence review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review.PloS one · 2026Pooled it
- Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.Critical care (London, England) · 2025Pooled it
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Early Sepsis Detection Using Heterogeneous Structured ICU Data with Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification.European heart journal. Digital health · 2026Article
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- Decoding cardiovascular risk in Chinese middle-aged and elderly adults: a 9-year prospective study integrating machine learning with explainable AI based on CHARLS cohort.BMC medical informatics and decision making · 2026Article
- Machine learning framework for predicting athletic injuries and optimising performance.BMC sports science, medicine & rehabilitation · 2026Article
- Prescribing a dose of transparency: a qualitative evaluation of AI explanations with cardiovascular healthcare professionals.Frontiers in artificial intelligence · 2026Article
- Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications.World journal of hepatology · 2025Review
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Explainable AI in early autism detection: a literature review of interpretable machine learning approaches.Discover mental health · 2025Review
- Explainable Artificial Intelligence in Radiological Cardiovascular Imaging-A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Large Language Models in Genomics-A Perspective on Personalized Medicine.Bioengineering (Basel, Switzerland) · 2025Review
- Artificial Intelligence in Ischemic Heart Disease Prevention.Current cardiology reports · 2025Review
- Transcatheter Aortic Valve Replacement in Bicuspid Aortic Valve Disease: A Review of the Existing Literature.Cureus · 2025Review
- Multi-Level Explainable AI for ECG-Based Atrial Fibrillation Detection: Exploring LIME, SHAP, and Grad-CAM for Clinical Interpretation.Proceedings of the 2025 9th International Conference on Medical and Health Informatics. International Conference on Medical and Health Informatics (9th : 2025 : Kyoto, Japan) · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models. Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-024-10852-w.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.