ReviewBJR artificial intelligence2025
Educational insights of artificial intelligence assisted chest radiograph interpretation in lung cancer detection-pictorial review.
Review in BJR artificial intelligence, 2025. 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
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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.
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
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest radiographs (CXRs) are one of the most complicated, yet most frequently performed imaging examinations worldwide, and are susceptible to reporting discrepancies, especially in the detection of lung cancer (LC). The use of artificial intelligence (AI) as a decision-supporting tool for CXRs has been shown to aid earlier LC detection and reduce reporting discrepancies. Nevertheless, the use of AI within radiology is still in its infancy in terms of clinical implementation. There is a need for more pragmatic evidence to enhance key stakeholders' perceptions and understating of AI within clinical practice. Most AI studies focus on comparing sensitivity, specificity, and accuracy against a re-review of pre-selected images by radiologists, often paired with complex technical data and statistical analyses. Improved understanding and awareness of AI capabilities whilst learning from AI discrepancies could help optimize human-AI interactions and acceptance within clinical practice whilst informing future research. The aim of this educational review is to provide a visual portrayal of real-world examples of missed LC cases from within a retrospective AI study, explore the impact of image quality on AI performance, and highlight cases of AI discrepancies.
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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 OpenQuestion graph.