ArticlePloS one2025
The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Human-AI Collaboration in Healthcare: A Scoping Review.NPJ digital medicine · 2026Article
- Radiology artificial intelligence for prioritized imaging and diagnosis of lung cancer: qualitative interview analysis of stakeholder perspectives in Northern Ireland.Frontiers in medicine · 2026Article
- An experimental machine learning study investigating the decision-making process of students and qualified radiographers when interpreting radiographic images.PLOS digital health · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence decision support systems have been proposed to assist a struggling National Health Service (NHS) workforce in the United Kingdom. Its implementation in UK healthcare systems has been identified as a priority for deployment. Few studies have investigated the impact of the feedback from such systems on the end user. This study investigated the impact of two forms of AI feedback (saliency/heatmaps and AI diagnosis with percentage confidence) on student and qualified diagnostic radiographers' accuracy when determining binary diagnosis on skeletal radiographs. The AI feedback proved beneficial to accuracy in all cases except when the AI was incorrect and for pathological cases in the student group. The self-reported trust of all participants decreased from the beginning to the end of the study. The findings of this study should guide developers in the provision of the most advantageous forms of AI feedback and direct educators in tailoring education to highlight weaknesses in human interaction with AI-based clinical decision support systems.
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Registered trials
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