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TRUVACE RECORD VERSION
record: TRV-2026-0764
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-08-15T06:21:15.103697Z
status: published
lens: g_space
sector: health
headline: Impact of AI assistance on reading time, cancer detection rate, and abnormal interpretation rate in screening and diagnostic mammography: a prospective alternating-month study
dek: Objective 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. Materials and methods 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 in…
gain_title: AI assistance during routine mammography was associated with higher overall cancer detection and no increase in average interpretation time.
problem_title: (none)
trace_subject: (none)
gain_reading: AI assistance during routine mammography was associated with higher overall cancer detection and no increase in average interpretation time.
gain_evidence: overall CDR was higher during the AI-assisted period (22.3 vs 11.5 per 1000; p = 0.005) | AI assistance in routine mammography interpretation was not associated with prolonged reading time and was associated with a higher overall CDR
problem_reading: (none)
problem_evidence: (none)
quick_read: Between August 2023 and July 2024, four radiologists interpreted 4577 screening and diagnostic mammograms in a prospective alternating-month design where a commercial AI system was shown or hidden. Reading times from PACS logs, cancer detection rates, and abnormal interpretation rates were compared between AI-assisted and non-AI-assisted months, with reading time analysis restricted to 2917 cases under five minutes.

The findings matter because they suggest AI can improve cancer detection in everyday mammography without slowing workflow, which is relevant to screening program effectiveness. Uncertainty remains about why abnormal interpretations rose only for diagnostic exams, whether results generalize beyond four readers and one practice, and how the filtered reading-time subset affects workflow conclusions.
limitation: 
tag: Evidence-backed gain
key_points: Prospective alternating-month design Aug 2023 to July 2024 with 4577 mammography examinations, mean age 51.7 7 10.4 years, read by four radiologists. | Commercially available AI system integrated into clinical workflow with results displayed or hidden on a monthly basis; reading time extracted from PACS log. | Reading time analysis used 2917 examinations with times 264 5 min to reduce non-interpretive interruptions. | Screening AIR unchanged at 9.5% vs 8.4% while diagnostic AIR rose to 18.7% vs 12.1% during AI-assisted months.
rundown: The study prospectively alternated months with AI results displayed versus hidden across screening and diagnostic two-view full-field digital mammography in routine practice from August 2023 to July 2024.

Among 4577 examinations, overall CDR doubled during AI-assisted months, screening AIR showed no significant difference, diagnostic AIR increased significantly, and mean reading time remained around 65 seconds in both periods.
sources:
- peer_reviewed | European Radiology | https://doi.org/10.1007/s00330-026-12793-0 | 2026-08-13
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