ReviewDiagnostics (Basel, Switzerland)2026
Bridging Pixels and Practice: AI-Assisted Mammography as a Next-Generation Diagnostic Strategy for Breast Cancer Screening.
Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Mammography is the cornerstone of population-based breast cancer screening, significantly improving prognosis and survival outcomes. However, limitations related to diagnostic accuracy, efficacy, and inequitable access persist. Recent advances in artificial intelligence (AI) have transformed AI-assisted mammography into a next-generation diagnostic strategy capable of enhancing screening performance within integrated diagnostic pathways. This narrative review examined whether AI-assisted mammography could serve as an effective next-generation diagnostic approach for breast cancer screening in global healthcare settings. Following PRISMA guidelines, studies were systematically screened using predefined eligibility criteria and quality appraisal with the CASP checklist, resulting in the inclusion of fourteen selected studies for analysis. Thematic synthesis identified three key domains: benefits, challenges, and stakeholder perspectives. AI-assisted mammography demonstrated improved cancer detection rates, enhanced diagnostic accuracy, reduced workload, and reduced recalls. However, challenges related to legal, ethical and social issues as well as transferability remain significant. Stakeholder perspectives emphasised the importance of human oversight, interdisciplinary collaboration, and workforce readiness. In conclusion, AI-assisted mammography offers significant promise as a next-generation diagnostic strategy, but successful translation into clinical practice requires substantial systemic, organisational, and practice-level changes across healthcare settings.
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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.