ArticleFrontiers in artificial intelligence2026
Prescribing a dose of transparency: a qualitative evaluation of AI explanations with cardiovascular healthcare professionals.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Chronic diseases, particularly heart failure, are associated with high mortality rates and frequent hospital readmissions, thereby placing a significant burden on healthcare systems and patients. Recent advancements in AI-supported telemonitoring offer a means to address these challenges through the early detection of deterioration. Yet, the black-box nature of AI can undermine physicians' trust, underscoring the importance of explainable AI (XAI). Methods: This exploratory study examines clinicians' perceptions of XAI in a prototype early-warning scenario for imminent heart failure decompensation based on tabular laboratory data. Three exemplary explanations are compared in interviews with 15 German cardiovascular healthcare professionals using the explanation satisfaction scale: SHapley Additive Explanations (SHAP), Counterfactual Explanations (CFEs), and Anchors. Results: SHAP was widely preferred for its intuitive graphical representation. CFEs were valued as an action-oriented method although their suitability for deriving therapeutic steps requires careful constraint and interpretation. Meanwhile, Anchors were considered too superficial. Discussion: Our findings suggest that no single explanation method fully meets clinicians' needs. Instead, layered explanation designs that combine quick overviews with optional deeper insights may better support trustworthy AI use in early-warning scenarios based on tabular laboratory data.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.