{"id":"AIBD-0644","title":"PRAIM: radiologists using Vara MG in the German mammography screening program detect 17.6% more breast cancers without more recalls","summary":"Across 463,094 women at 12 German screening sites, radiologists who used Vara MG found 6.7 cancers per 1,000 against 5.7 without it, with no rise in recalls.","date_observed":"2023-02-01","date_basis":"event","date_precision":"month","benefit_status":"reported","source_collection":null,"source_entry":null,"account":"The PRAIM study followed routine mammography screening of women aged 50 to 69 at 12 sites of the German screening program from July 2021 to February 2023. Radiologists chose for each examination whether to read it in the Vara MG viewer, a CE-certified product from the Berlin company Vara. The software tagged examinations that it judged normal and raised a safety-net alert on examinations that it judged suspicious. Of 463,094 women, 260,739 were read with AI support. Radiologists who used the AI found 6.7 cancers per 1,000 women against 5.7 per 1,000 without it, a 17.6% higher rate. The recall rate was 37.4 per 1,000 with AI and 38.3 per 1,000 without. The safety net led to 204 cancer diagnoses that the authors judge would otherwise have been missed. The study was observational, so radiologists chose when to use the AI, and the authors report a reading-behavior bias. Vara funded the study, took part in its design and writing, and employed three of the authors.","category":"general benefit","unexpected":false,"purpose":"Vara MG was built to display mammograms and to preclassify screening examinations as normal or suspicious for radiologists. The higher detection rate was that purpose.","system":"Vara MG","model_version":null,"developer":"Vara","deployer":"Radiologists at 12 screening units of the German mammography screening program","location":"12 screening sites, Germany","domain":"Healthcare","segment":null,"benefit_type":"Improved Task Accuracy","taxonomy_note":null,"registry_ids":{},"ratings":[{"group":"personal","depth":4,"reason":"The safety net led to 204 breast cancer diagnoses among women whose examinations radiologists had first read as normal."},{"group":"organizational","depth":2,"reason":"Detection was 6.7 per 1,000 with AI against 5.7 per 1,000 without, with a recall rate that did not rise."}],"ai_role":{"status":"assessed","statement":"The study compares AI-supported double reading with standard double reading in the same program and period. It is not randomized, because radiologists chose when to use the AI, so confounding is possible."},"evidence":[{"kind":"peer-reviewed study","citation":"Ferre R, Benefield T, Kuzmiak CM. Artificial intelligence-supported double reading in European population breast cancer screening: a systematic review and meta-analysis of prospective programs. Clin Imaging. 2026;139:110923. doi:10.1016/j.clinimag.2026.110923","title":"Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs","url":"https://pubmed.ncbi.nlm.nih.gov/42617468/","date":"2026-08-11","written_by":"independent","quote":"in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed"},{"kind":"peer-reviewed study","citation":"Ciurescu S, Buciu V, Ilaș DG, Pârvănescu R, Șerban D. Ten years of artificial intelligence in screening mammography: a systematic review and meta-analysis of diagnostic accuracy and clinical implementation (literature published 2015-2025). Diagnostics (Basel). 2026;16(18):3045. doi:10.3390/diagnostics16183045","title":"Ten Years of Artificial Intelligence in Screening Mammography: A Systematic Review and Meta-Analysis of Diagnostic Accuracy and Clinical Implementation (Literature Published 2015-2025)","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13605948/","date":"2026-09-20","written_by":"independent","quote":"The largest by far is PRAIM, a prospective evaluation across twelve German screening sites, in which 463,094 women were screened and radiologists chose voluntarily whether to use AI support; detection was 6.7 versus 5.7 per 1000, a ratio of 1.18 (1.06-1.31), with recall slightly lower and non-inferior"},{"kind":"peer-reviewed study","citation":"Eisemann N, Bunk S, Mukama T, et al. Nationwide real-world implementation of AI for cancer detection in population-based mammography screening. Nat Med. 2025;31:917-924. doi:10.1038/s41591-024-03408-6","title":"Nationwide real-world implementation of AI for cancer detection in population-based mammography screening","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11922743/","date":"2025-01-07","written_by":"makers","quote":"Radiologists in the AI-supported screening group achieved a breast cancer detection rate of 6.7 per 1,000, which was 17.6% (95% confidence interval: +5.7%, +30.8%) higher than and statistically superior to the rate (5.7 per 1,000) achieved in the control group."}],"replication":{"produced_by":["Radiologists read in an AI-supported viewer that tagged likely normal examinations.","A safety-net alert asked radiologists to look again at examinations that the AI judged suspicious.","Final recall decisions stayed with radiologists in a double-reading program."],"must_hold":["Radiologists must accept a share of safety-net alerts and review them.","The normal tag must not lead radiologists to miss cancers in tagged examinations.","The AI must perform on the local population as it did in Germany."],"echoes":[{"date":"2022-12-07","setting":"MASAI randomized trial, southwest Sweden (Transpara)","model_version":null,"outcome":"recurred","source":"https://pubmed.ncbi.nlm.nih.gov/39904652/"},{"date":"2022-06-09","setting":"ScreenTrustCAD, Stockholm (Lunit Insight MMG)","model_version":null,"outcome":"recurred","source":"https://pubmed.ncbi.nlm.nih.gov/37690911/"}]},"paired_incident":null,"headline":"*Vara MG* helps German screening radiologists find more breast cancers","metric":"found 6.7 cancers per 1,000 against 5.7 without it","comparator":{"against":"Standard double reading without AI in the same program","before":"5.7 cancers per 1,000 women","after":"6.7 cancers per 1,000 women","basis":"measured","quote":"Radiologists in the AI-supported screening group achieved a breast cancer detection rate of 6.7 per 1,000","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11922743/"},"later":null,"value":null,"developer_country":"Germany","deployer_country":"Germany","unrated_because":null,"brief_notes":null,"brief":null,"revisions":[{"date":"2026-10-29","change":"Added the case.","source":"https://pubmed.ncbi.nlm.nih.gov/42617468/","editor":"AIBD"}],"confidence":"studied","shade":"high","claim_status":"Independently studied","claim_basis":"Sources: two independent peer-reviewed studies and the makers' peer-reviewed study. 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