ArticleJMIR human factors2026
The Role of Explanations in AI-Generated Alerts: Qualitative Study of Clinical Views on Explainable AI in Predictive Tools.
Article in JMIR human factors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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7 authors.
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Abstract
Background: Artificial intelligence (AI)-driven clinical decision support (CDS) tools offer promising solutions for health care delivery by optimizing resource allocation, detecting deterioration, and enabling early interventions. However, adoption remains limited due to insufficient validation and a lack of transparency and trust. Explainable AI (XAI) seeks to improve user understanding of AI outputs; however, how clinicians interpret and integrate these explanations into their decision-making remains underexplored. Furthermore, discrepancies in explanations, known as the "disagreement problem," can undermine trust and, at worst, lead to poor clinical decisions. Objective: This study examines clinicians' perspectives on the role and value of explainability in AI-driven CDS tools within Australian critical care settings and the impact of discrepancies in AI-generated explanations on clinical decision-making. Methods: Qualitative data were collected using semistructured interviews with 14 clinical experts, incorporating scenario-based exercises, and were analyzed using inductive thematic analysis. Results: Clinicians valued explainability, particularly in complex or unfamiliar situations, when explanations were clear, plausible, and actionable. Trust and perceived usefulness extended beyond explanation quality, encompassing factors such as system accuracy, alignment with clinicians' reasoning, workflow integration, and perceived reliability. Discrepancies in explanations generated by different XAI methods were not a major concern, provided that the AI-generated predictive alerts were accurate. Conclusions: This study provides design recommendations for developing trustworthy, user-centric CDS tools that incorporate XAI. Findings highlight that explainability is critical for establishing initial trust in AI-driven tools by supporting perceived usefulness, but its importance diminishes over time and with user expertise and familiarity, as learned usefulness takes precedence. Recommendations highlight the importance of aligning the design and implementation of AI tools with clinicians' needs to enhance trust, mitigate risks, and promote successful adoption for improved patient outcomes.
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