ReviewEuropean journal of radiology open2026
Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.
Review in European journal of radiology open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- Human-AI interaction in a cancer-enriched double-reading breast screening cohort: diagnostic accuracy and second-reader behavior.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To compare AI-augmented and conventional double reading in organised breast-cancer screening with respect to cancer-detection rate (CDR), recall rate, and radiologist workload. Methods: We conducted a systematic review and random-effects meta-analysis of 13 prospective and retrospective studies (1.03 million screens) from 2017 to 2024 that embedded commercial or research AI into population-based digital mammography or tomosynthesis programmes. Eligible studies included ≥ 10,000 screens (or ≥100 cancers) and reported CDR, recalls, and/or workload metrics. We extracted cancer and recall counts and calculated risk ratios (RRs) for AI-augmented versus double reading, overall and by integration model: independent second reader, gate-keeper/decision-referral triage, and concurrent overlay. Results: Overall, AI-augmented protocols achieved CDR parity (RR 1.01; 95 % CI 0.96-1.07) and no significant change in recalls (RR 1.00; 95 % CI 0.88-1.15). Triage models preserved CDR (RR 1.02; 95 % CI 0.98-1.07) while reducing recalls by 11 % (RR 0.89; 95 % CI 0.82-0.96) and cutting initial reads by 44-70 %. Independent-reader workflows maintained CDR (RR 0.98; 95 % CI 0.92-1.05) but showed variable recall effects (RR 1.12; 95 % CI 0.90-1.39) driven by arbitration logic and threshold choices. Concurrent overlay (two studies) indicated possible sensitivity gains (RR 1.31; 95 % CI 0.90-1.91) without higher recall rates, though precision was limited. Conclusions: AI integration can match conventional double reading in detection performance, but its impact on workflow depends on the chosen model. Triage-based approaches consistently lower radiologist workload and recalls without compromising sensitivity, whereas replacing a second reader may simply shift effort to arbitration. Future implementation should focus on workflow-aware metrics and prospective threshold validation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.