ArticleAbdominal radiology (New York)2026
Improving radiology reporting accuracy: use of GPT-4 to reduce errors in reports.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Can AI write reports like a radiologist? A blinded evaluation of large language model-generated lumbar spine MRI reports.European radiology experimental · 2026Observational
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Authors and funding
13 authors.
Funding
Abstract
purposeRadiology reports are essential for communicating imaging findings to guide diagnosis and treatment. Although most radiology reports are accurate, errors can occur in the final reports due to high workloads, use of dictation software, and human error. Advanced artificial intelligence models, such as GPT-4, show potential as tools to improve report accuracy. This retrospective study evaluated how GPT-4 performed in detecting and correcting errors in finalized radiology reports in real-world settings for abdominopelvic computed tomography (CT) reports.
methodsWe evaluated finalized CT abdominopelvic reports from a tertiary health system by using GPT-4 with zero-shot learning techniques. Six radiologists each reviewed 100 of their finalized reports (randomly selected), evaluating GPT-4's suggested revisions for agreement, acceptance, and clinical impact. The radiologists' responses were compared by years in practice and sex.
resultsGPT-4 identified issues and suggested revisions for 91% of the 600 reports; most revisions addressed grammar (74%). The radiologists agreed with 27% of the revisions and accepted 23%. Most revisions were rated as having no (44%) or low (46%) clinical impact. Potential harm was rare (8%), with only 2 cases of potentially severe harm. Radiologists with less experience (≤ 7 years of practice) were more likely to agree with the revisions suggested by GPT-4 than those with more experience (34% vs. 20%, P = .003) and accepted a greater percentage of the revisions (32% vs. 15%, P = .003).
conclusionsAlthough GPT-4 showed promise in identifying errors and improving the clarity of finalized radiology reports, most errors were categorized as minor, with no or low clinical impact. Collectively, the radiologists accepted 23% of the suggested revisions in their finalized reports. This study highlights the potential of GPT-4 as a prospective tool for radiology reporting, with further refinement needed for consistent use in clinical practice.
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