What Makes Cardiac AI Explainable Enough for Use?
A narrative review surveys AI across ECG and cardiac imaging, and considers what trustworthy clinical integration requires beyond accuracy.
Accuracy is only one part of trust
Cardiology AI spans electrocardiography, echocardiography, CT, MRI, risk prediction, and care workflows. A model can perform well on a benchmark and still be difficult to use if clinicians cannot understand its output, if it is poorly calibrated, or if it behaves differently in a new hospital.
A narrative review in the Journal of Clinical Medicine examines clinical and organizational AI applications in cardiology, drawing on literature published from 2015 through 2026. It is a synthesis of prior work, not a new diagnostic trial or a pooled estimate of model performance.
Explainability in the workflow
The review places explainability alongside reliability, data quality, and operational fit. In practice, a useful explanation should help a clinician assess what evidence contributed to an output and when that output should be questioned. It should not be treated as a guarantee that a model is correct.
For ECG and imaging systems, evaluation therefore needs more than a single accuracy score. External validation, subgroup performance, calibration, failure analysis, and a clear account of how a result fits into existing decisions all matter. Governance also includes privacy, accountability, and monitoring for changes after deployment.
A practical reading of the review
Because the paper is a narrative review, its conclusions depend on the studies it selected and how the authors organized them. It does not establish that one explanation method is superior or that an explainable system improves patient outcomes.
The useful takeaway is a checklist mindset: ask what signal or image the model sees, how uncertainty is communicated, who is responsible for acting on the result, and how performance will be monitored locally. Interpretability is one component of trustworthy AI—not a substitute for clinical evidence.
Source and credit
Independent summary of “Explainable and Trustworthy Artificial Intelligence in Cardiology: A Narrative Review of Clinical Applications, Operational Integration, and Future Directions,” Journal of Clinical Medicine, published 23 June 2026. Original paper, DOI: 10.3390/jcm15134885. Source article license: CC BY 4.0. No source figures or tables are reproduced here.
Questions about this article? Contact the author. Research discussion is welcome; please do not submit personal medical information.