Artificial intelligence–enabled liquid biopsy in cancer: a systematic review and meta- analysis of diagnostic performance and biological implications

Background: Liquid biopsy offers a minimally invasive approach for cancer detection and monitoring, but its diagnostic performance is often limited by low signal abundance and biological noise. Artificial intelligence (AI) has been proposed as a computational framework to integrate heterogeneous and biologically noisy circulating biomarker data; however, the biological underpinnings, clinical relevance, and translational meaning of the reported performance gains remain unclear. Methods: We conducted a systematic review and random-effects meta-analysis of studies published between 2022 and 2025 applying AI-based methods to liquid-biopsy data for oncologic diagnosis. Extracted outcomes included AUROC, sensitivity, specificity, cancer type, analyte class, and AI model. Pooled AUROC and absolute AUROC differences between AI-based and non-AI approaches were estimated with 95% confidence intervals using a random-effects model. AUROC values were logit-transformed prior to pooling to stabilize variance and were subsequently back-transformed for presentation. Statistical heterogeneity was quantified using the I² statistic. Results: Twenty-eight studies across multiple tumor types met inclusion criteria, of which ten provided extractable data for quantitative synthesis. AI-enhanced liquid biopsy achieved a pooled AUROC of 0.924 (95% CI, 0.879-0.953). When head-to-head comparisons were available, AI-based models demonstrated an absolute AUROC improvement of 0.025 (95% CI, 0.019-0.030) compared with conventional analytical approaches. While numerically modest, this improvement was consistent across cancer types and reflects AI's ability to extract complementary diagnostic signal from complex circulating biomarkers. Between-study heterogeneity was substantial (I² = 88.8%). Conclusions: AI-enhanced liquid biopsy achieved a pooled AUROC of 0.924 (95% CI, 0.879-0.953), reflecting AI's ability to integrate multiple weak and complementary biological signals rather than reliance on single circulating biomarkers. These findings support AI's potential role as a clinical decision- support tool in molecular diagnostics; however, translation into practice will require prospectively validated, clinically calibrated models aligned with specific diagnostic intents. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier CRD420251163071.