Smartphone-Based Artificial Intelligence Colposcopy for Cervical Cancer Screening in Low-Resource Settings: Protocol and Preliminary Findings From a Systematic Review and Meta-Analysis [ID 3846]

INTRODUCTION: Cervical cancer disproportionately affects women in low- and middle-income countries (LMICs), where access to cytology, human papillomavirus (HPV) DNA testing, and expert colposcopy remains limited. Advances in smartphone imaging and artificial intelligence (AI) algorithms offer a scalable, low-cost alternative for screening. We present the protocol for a systematic review and meta-analysis evaluating the diagnostic accuracy of smartphone-based AI colposcopy in LMICs, with preliminary findings from our ongoing review. METHODS: This review is registered in PROSPERO (ID CRD420251142715) and follows PRISMA-DTA guidelines. Eligible studies include randomized and non-randomized diagnostic accuracy studies assessing smartphone-based AI colposcopy or visual inspection with acetic acid (VIA) compared with reference standards (histopathology, expert colposcopy, cytology, HPV DNA testing). Databases searched are PubMed/MEDLINE, Embase, CENTRAL, CINAHL, LILACS, Scopus, and IEEE Xplore (2010 to present). Dual independent reviewers conduct screening, extraction, and bias assessment using QUADAS-2. Pooled sensitivity and specificity will be estimated using a bivariate random-effects model, with summary ROC curves generated. RESULTS: As of January 2025, more than 1,200 records were identified, with 46 studies undergoing full-text review. Early synthesis suggests smartphone-based AI achieves high diagnostic accuracy, with sensitivities of 85–98% and specificities of 75–92% versus histopathology or expert colposcopy. Feasibility studies emphasize offline functionality, task-shifting potential to nonspecialists, and strong patient acceptability. CONCLUSIONS/IMPLICATIONS: Smartphone-based AI colposcopy shows strong promise for expanding cervical cancer screening in resource-limited settings. This review will provide pooled diagnostic accuracy estimates and identify research gaps, informing clinical implementation and policy in line with the World Health Organization’s elimination strategy.