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Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study
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DOI：10.1007/s00330-026-12793-0.png)
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Abstract
En 中文
To compare reading time, cancer detection rate (CDR), and abnormal interpretation rate (AIR) between AI-assisted and non-AI-assisted periods in screening and diagnostic mammography performed in routine clinical practice. We prospectively collected reading times for consecutive two-view full-field digital mammography interpreted by four radiologists between August 2023 and July 2024. Both screening and diagnostic examinations were included. A commercially available AI system was integrated into the clinical workflow, with results displayed or hidden on a monthly basis. Reading time, CDR, and AIR were compared between two periods. For reading time analysis, a subset of 2917 examinations with times ≤ 5 min was included to minimize the impact of non-interpretive interruptions. Reading time was extracted from the PACS log. Among 4577 mammography examinations (mean age 51.7 ± 10.4 years), the overall CDR was higher during the AI-assisted period (22.3 vs 11.5 per 1000; p = 0.005). AIR did not differ for screening mammography (9.5% vs 8.4%; p = 0.293) but was higher with AI assistance for diagnostic mammography (18.7% vs 12.1%; p = 0.026). Mean reading times were comparable between AI-assisted and non-AI-assisted periods (65.0 vs 64.3 s; p = 0.723). AI assistance in routine mammography interpretation was not associated with prolonged reading time and was associated with a higher overall CDR. Question Does artificial intelligence assistance in routine mammography affect radiologists’ reading time, cancer detection, or abnormal interpretation in daily practice? Findings Artificial intelligence assistance was not associated with prolonged reading time and was associated with a higher overall cancer detection rate. Abnormal interpretation increased only for diagnostic examinations, with no difference in screening examinations. Clinical relevance AI assistance may support screening mammography by aiding cancer detection without disrupting workflow, whereas diagnostic use should be applied carefully due to higher abnormal interpretation rates.
Keywords:
Artificial intelligence, Breast cancer
Diagnosis, computer-assisted
Digital mammography
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