ArticleArtificial intelligence2023
Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making.
Article in Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 5 of them syntheses that pooled it.
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
39 citing papers in PubMed, 5 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
- From interface to outcome: a 4I framework for AI-linked functionality in electronic health records.BMC medical informatics and decision making · 2026Pooled it
- The application of machine learning in predicting post-cardiac surgery acute kidney injury in pediatric patients: a systematic review.Frontiers in pediatrics · 2025Pooled it
- Human-centered evaluation of explainable AI applications: a systematic review.Frontiers in artificial intelligence · 2024Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial.JMIR medical informatics · 2025Trial
- Uncertainty-aware and interpretable deep learning in cytopathology: toward trustworthy AI-powered diagnostics.Virchows Archiv : an international journal of pathology · 2026Review
- Sustaining High Reliability Amid Artificial Intelligence Adoption in Oncology.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2026Article
- Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Leveraging human-centered AI for clinical decision-making: a transparent, accurate rule extractor using non-dominated sorting genetic algorithm.BMC medical informatics and decision making · 2026Article
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- The Role of Explanations in AI-Generated Alerts: Qualitative Study of Clinical Views on Explainable AI in Predictive Tools.JMIR human factors · 2026Article
- Enhancing dental clinical education through AI: insights into post-clinical summaries.BMC medical education · 2026Article
- Designing a Digital Twin for the Management of Noncommunicable Diseases: Protocol for a Pilot Study and Methodology Validation.JMIR research protocols · 2026Article
- Review
- Artificial Intelligence Integration in Multidisciplinary Wound Management: A Scoping Review of Barriers and Facilitators in Clinical Workflows.Journal of multidisciplinary healthcare · 2026Review
- Hyper-selective explainability: an empirical case study of the utility of explainability in a clinical decision support system.AI and ethics · 2026Article
- Investigating How Clinicians Form Trust in an AI-Based Mental Health Model: Qualitative Case Study.JMIR human factors · 2025Article
- Real-World Monitoring of Artificial Intelligence in Radiology: Challenges and Best Practices.Korean journal of radiology · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support. The study revealed a more nuanced role for ML explanation models, when these are pragmatically embedded in the clinical context. Despite the general positive attitude of healthcare professionals (HCPs) towards explanations as a safety and trust mechanism, for a significant set of participants there were negative effects associated with confirmation bias, accentuating model over-reliance and increased effort to interact with the model. Also, contradicting one of its main intended functions, standard explanatory models showed limited ability to support a critical understanding of the limitations of the model. However, we found new significant positive effects which repositions the role of explanations within a clinical context: these include reduction of automation bias, addressing ambiguous clinical cases (cases where HCPs were not certain about their decision) and support of less experienced HCPs in the acquisition of new domain knowledge.
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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 Socratic graph.