ArticleAbdominal radiology (New York)2026
Automated generation of impressions in abdominal radiology reports using an artificial intelligence-based tool: performance compared to manual impressions.
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. Not yet cited in PubMed.
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15 authors.
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Abstract
objectivesRecent years have seen a rapid development of artificial intelligence (AI) tools to enhance radiologists' workflow, but most have focused on image acquisition and interpretation. Generating radiology reports is a critical element of practice but remains a source of inefficiency and cognitive load. PURPOSE: To investigate the performance of an AI based tool integrated into dictation software to automate generation of report impressions and compare it with radiologist generated impressions.
methodsOne hundred consecutive abdominal radiology reports were retrospectively selected in January 2024 from a single center. An AI-generated impression (GI) was created for each report and compared with the original radiologist impression (RI). Ten subspecialty abdominal radiologists evaluated the blinded, randomized pairs. Each impression was evaluated on a 5-point Likert scale for coherence, comprehensiveness, and factual consistency, and an overall preference for the impression was recorded. Cumulative link mixed models and binomial logistic regression were used where appropriate.
resultsGI was preferred in 38% of reports (95% CI 32-45%), RI in 45% (95% CI 39-50%), with no preference in 17% of instances. Mixed-effects logistic regression demonstrated that GI was rated as equivalent or preferred to RI (odds ratio 1.34, 95% CI 1.004-1.788, p = 0.02). Radiologists rated GI equivalent or superior to RI in 79% of cases for coherence, 66% for comprehensiveness, and 77% for factual consistency.
conclusionThe AI generated impressions were clinically acceptable and rated equivalent or superior to radiologist-generated impressions in the majority of cases across key quality metrics.
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