AI/ML-based miRNA signatures show diagnostic potential for early breast cancer detectionCould a simple blood test help find early breast cancer with high accuracy?

BackgroundEarly breast cancer detection remains central to improving clinical outcomes, yet conventional screening pathways, particularly mammography, have recognized limitations in sensitivity, specificity, and performance in dense breast tissue. Circulating microRNAs (miRNAs) have emerged as promising minimally invasive biomarkers, while artificial intelligence and machine learning (AI/ML) offer powerful tools for identifying diagnostically relevant multi-marker patterns within complex biomarker datasets. This systematic review and meta-analysis evaluated the diagnostic performance of AI/ML-based circulating miRNA signatures for early breast cancer detection. MethodsA systematic search of PubMed/MEDLINE, Scopus, and Web of Science Core Collection was conducted from database inception to 31 December 2025. Studies were eligible if they were original human investigations evaluating circulating miRNAs using an AI/ML-based diagnostic model for breast cancer detection and reporting extractable diagnostic performance metrics. Study selection followed PRISMA 2020 and PRISMA-DTA guidance. Methodological quality was assessed using QUADAS-2. Pooled sensitivity and specificity were synthesized using a bivariate random-effects model, and overall diagnostic performance was summarized using a hierarchical summary receiver operating characteristic framework. ResultsSeven studies met the inclusion criteria for qualitative synthesis, with eligible studies contributing to the quantitative analysis depending on data availability. Across the pooled analysis, AI/ML-based circulating miRNA models demonstrated good overall diagnostic performance, with a pooled AUC of 0.905 (95% CI: 0.890-0.921), pooled sensitivity of 81.3% (95% CI: 76.8%-85.2%), and pooled specificity of 87.0% (95% CI: 82.4%-90.7%). Heterogeneity was moderate for AUC (I{superscript 2} = 42.3%) and sensitivity (I{superscript 2} = 38.7%) and low for specificity (I{superscript 2} = 28.4%). Risk-of-bias assessment showed overall low-to-moderate methodological concern, with patient selection representing the most variable domain. Deeks funnel plot asymmetry test showed no significant evidence of publication bias (p = 0.34). ConclusionsAI/ML-based circulating miRNA signatures show promising diagnostic accuracy for early breast cancer detection and may have value as non-invasive adjunctive tools within imaging-supported diagnostic pathways. However, the evidence base remains limited by methodological heterogeneity, variable validation rigor, and the predominance of retrospective case-control designs. Prospective, standardized, and externally validated studies are needed before routine clinical implementation can be justified.