SynthesisJournal of medical Internet research2025
Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption.
Synthesis in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 4 of them syntheses that pooled it.
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
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
22 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial intelligence for risk-stratified breast cancer screening: a systematic review of evidence, clinical integration, and ethical implications in risk assessment tools.La Radiologia medica · 2026Pooled it
- Deep learning for detecting early gastric cancer with white-light endoscopy: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Performance of Automatic Speech Analysis in Detecting Depression: Systematic Review and Meta-Analysis.JMIR mental health · 2025Pooled it
- AI for IMPACTS Framework for Evaluating the Long-Term Real-World Impacts of AI-Powered Clinician Tools: Systematic Review and Narrative Synthesis.Journal of medical Internet research · 2025Pooled it
- Side-level versus patient-level evaluation in four-view mammography classification: a comprehensive benchmark on the CMMD dataset.Future science OA · 2026Article
- Implementation of an artificial intelligence-based system for mammography in the compulsory medical insurance program: results of a 3-year study.Quantitative imaging in medicine and surgery · 2026Article
- Effect of evaluation prompt strategies on LLM-as-a-judge reliability in critical care.Anaesthesiology intensive therapy · 2026Article
- Operationalizing the EU AI Act in a comprehensive cancer center through an institutional governance framework.NPJ digital medicine · 2026Review
- Hybrid metaheuristic feature selection for breast cancer detection in digital mammography: a radiomics and deep learning pilot feasibility study.BMC medical imaging · 2026Article
- Barriers, Facilitators, and Intention to Use AI for Breast Cancer Diagnosis: Mixed Methods Study Among Austrian Physicians With and Without AI Experience.Journal of medical Internet research · 2026Article
- Patient and Public Perceptions of Artificial Intelligence in Breast Imaging and Clinical Decision-Making: An Exploratory Cross-Sectional Survey Study.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Artificial intelligence in breast surgery: Current applications, challenges, and future perspectives.The Journal of international medical research · 2026Review
- HER2-Low Breast Cancer at the Interface of Pathology and Technology: Toward Precision Management.Biomedicines · 2025Review
- Artificial Intelligence-Driven Personalization in Breast Cancer Screening: From Population Models to Individualized Protocols.Cancers · 2025Review
- Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics.Cancer medicine · 2025Article
- Transforming Breast Imaging: A Narrative Review of Systematic Evidence on Artificial Intelligence in Mammographic Practice.Diagnostics (Basel, Switzerland) · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) studies show promise in enhancing accuracy and efficiency in mammographic screening programs worldwide. However, its integration into clinical workflows faces several challenges, including unintended errors, the need for professional training, and ethical concerns. Notably, specific frameworks for AI imaging in breast cancer screening are still lacking.
objectiveThis study aims to identify the challenges associated with implementing AI in breast screening programs and to apply the Consolidated Framework for Implementation Research (CFIR) to discuss a practical governance framework for AI in this context.
methodsThree electronic databases (PubMed, Embase, and MEDLINE) were searched using combinations of the keywords "artificial intelligence," "regulation," "governance," "breast cancer," and "screening." Original studies evaluating AI in breast cancer detection or discussing challenges related to AI implementation in this setting were eligible for review. Findings were narratively synthesized and subsequently mapped directly onto the constructs within the CFIR.
resultsA total of 1240 results were retrieved, with 20 original studies ultimately included in this systematic review. The majority (n=19) focused on AI-enhanced mammography, while 1 addressed AI-enhanced ultrasound for women with dense breasts. Most studies originated from the United States (n=5) and the United Kingdom (n=4), with publication years ranging from 2019 to 2023. The quality of papers was rated as moderate to high. The key challenges identified were reproducibility, evidentiary standards, technological concerns, trust issues, as well as ethical, legal, societal concerns, and postadoption uncertainty. By aligning these findings with the CFIR constructs, action plans targeting the main challenges were incorporated into the framework, facilitating a structured approach to addressing these issues.
conclusionsThis systematic review identifies key challenges in implementing AI in breast cancer screening, emphasizing the need for consistency, robust evidentiary standards, technological advancements, user trust, ethical frameworks, legal safeguards, and societal benefits. These findings can serve as a blueprint for policy makers, clinicians, and AI developers to collaboratively advance AI adoption in breast cancer screening.
trial registrationPROSPERO CRD42024553889; https://tinyurl.com/mu4nwcxt.
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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.