ArticleJournal of medical Internet research2026
Measuring the Impact of AI on Report-Drafting Efficiency in Chest Computed Tomography Interpretation: Retrospective Analysis.
Article in Journal of medical Internet research, 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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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.
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Who cites it
1 citing paper in PubMed.
- Large language models and large multimodal models in radiology: opportunities, challenges, and the path toward sustainable long-term clinical integration.Japanese journal of radiology · 2026Review
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6 authors.
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Abstract
backgroundArtificial intelligence (AI), particularly deep learning, has shown promise in enhancing medical image interpretation and improving radiologists' efficiency. In China, growing imaging demand and workforce shortages have placed increasing pressure on radiology services. However, evidence on the operational impact of AI on reporting efficiency remains limited.
objectiveThis study aimed to evaluate the effect of an AI system on radiologists' reporting efficiency by examining changes in report-drafting time for lung nodule diagnosis in chest computed tomography (CT) images.
methodsWe analyzed 185,044 chest CT reports from Beijing Anzhen and Tsinghua Changgung Hospitals (2018-2023) using a difference-in-differences design with nonequivalent comparison groups. Report-drafting time before, immediately after, and up to 2 years following AI implementation was compared, adjusting for radiologist gender, seniority, and years of working experience.
resultsThe pooled analysis showed a modest overall increase of 0.86 minutes (95% CI 0.14 to 1.57). However, this masked substantial heterogeneity between hospitals due to differing implementation timelines. In the first year after AI deployment, Tsinghua Changgung Hospital experienced a nonsignificant increase of 0.90 minutes (95% CI -0.28 to 2.08). In contrast, at Beijing Anzhen Hospital, the AI-assisted group exhibited an absolute reduction of 0.76 minutes by the first year and a further 1.83-minute reduction by the second year (an approximate 28% time saved vs baseline), while the control group remained stable over time. Using a difference-in-differences framework, this corresponded to a 2.66-minute relative improvement compared with the counterfactual trend (P<.001).
conclusionsAI-assisted lung nodule diagnosis may initially increase report-drafting time due to adaptation and workflow adjustment. Sustained, meaningful efficiency gains were heterogeneous and observed at only 1 of the 2 study sites, indicating that long-term impacts are strongly contingent on site-specific implementation dynamics, learning curves, and local context.
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