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Artificial intelligence in cervical cancer screening and triage: a role-stratified systematic review and bivariate
Murat Cengiz1, Onur Can Zaim1, Bilal Esat Temiz1
1Department of Obstetrics and Gynecology, Division of Gynecologic Oncology, Faculty of Medicine, Hacettepe University, Ankara, Türkiye.
Purpose Of Review:
Artificial intelligence (AI) tools for cervical cancer screening have proliferated, but modality-pooled accuracy estimates conflate clinically distinct uses of AI. We re-examine this evidence base through a role-stratified bivariate meta-analysis to clarify where AI is ready for clinical translation and where gaps remain.
Recent Findings:
Of 97 eligible studies published between 2019 and 2026, 47 with reconstructible 2 × 2 data were pooled using a bivariate Reitsma model stratified by clinical role. Diagnostic assistance during colposcopy showed the most mature evidence ( k = 21; sensitivity 0.908, specificity 0.844; HSROC AUC 0.94). Primary AI-cytology screening showed high sensitivity but unstable specificity ( k = 10; 0.934/0.701 [0.460-0.865]; AUC 0.92). Triage of hrHPV-positive women was the smallest and weakest pool ( k = 4; sensitivity 0.805, 95% CI lower bound 0.624 - below the 90% safety threshold commonly cited for HPV-positive triage). External validation was reported in 10.9% of studies and 49.1% originated from China. Deeks' funnel asymmetry was borderline for screening ( P = 0.082) and significant for diagnostic assistance ( P = 0.007).
Summary:
AI is closest to translation as diagnostic assistance during colposcopy. PosthrHPV triage - not primary screening - is the critical evidence gap. Future work should prioritize prospective multicentre multimodal (HPV + AI cytology + orthogonal biomarker) risk-calibrated triage models over further modality-pooled accuracy studies.