ReviewCureus2025
Screening Mammography and Artificial Intelligence: A Comprehensive Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Clinical Performance Tradeoffs of ChatGPT-5.2 Thinking (OpenAI) Compared with Radiologist Interpretation in Biopsy-Referred Mammography: Cancer Detection, False Positives, and Laterality.Tomography (Ann Arbor, Mich.) · 2026Article
- Enhancing Automated Breast Cancer Detection: A CNN-Driven Method for Multi-Modal Imaging Techniques.Journal of personalized medicine · 2025Article
- Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice.Diagnostics (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Screening mammography is vital for early breast cancer detection, improving outcomes by identifying malignancies at treatable stages. Artificial intelligence has emerged as a tool to enhance diagnostic accuracy and reduce radiologists' workload in screening programs, though its full integration into clinical practice remains limited, necessitating a comprehensive review of its performance. This systematic review assesses artificial intelligence's effectiveness in screening mammography, focusing on diagnostic performance, reduction of false positives, and support for radiologists in clinical decision-making. A systematic search was conducted across PubMed, Embase, Web of Science, Cochrane Central, and Scopus for studies published between 2013 and 2024, including those evaluating artificial intelligence in mammography screening and reporting outcomes related to cancer detection, sensitivity, specificity, and workflow optimization. A total of 13 studies were analyzed, with data extracted on study characteristics, population demographics, artificial intelligence algorithms, and key outcomes. Artificial intelligence-assisted readings in screening mammography were found to be comparable or superior to traditional double readings by radiologists, reducing unnecessary recalls, improving specificity, and in some cases increasing cancer detection rates. Its integration into workflows showed potential for reducing radiologist workload while maintaining high diagnostic performance; however, challenges such as high false-positive rates and variations in artificial intelligence performance across patient subgroups remain concerns. Overall, artificial intelligence has the potential to enhance the efficiency and accuracy of breast cancer screening programs, and while it can reduce unnecessary recalls and alleviate radiologists' workloads, issues with false positives and demographic variations in accuracy highlight the need for further research. With ongoing refinement, artificial intelligence could become a valuable tool in routine mammography screening, augmenting radiologists' capabilities and improving patient care.
Indexed as
Identifiers
What OpenQuestion holds
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.