Concordance of AI-Assisted and Hybrid AI-Assisted Cervical Imaging Systems with Visual Inspection with Acetic Acid for Cervical Precancer Screening in West Bandung, Indonesia

Background: Cervical cancer remains a major cause of morbidity and mortality among women, particularly in low- and middle-income countries (LMICs) where screening access is limited. Visual Inspection with Acetic Acid (VIA) is widely used but subject to inter-observer variability. Artificial intelligence (AI)–assisted cervical imaging may improve standardization; however, real-world community evidence is limited. Objective: To evaluate the concordance of AI-assisted and hybrid AI-assisted cervical imaging systems (CIS) relative to VIA findings in a community-based cervical precancer screening program in West Bandung, Indonesia. Methods: A cross-sectional study was conducted among 71 women to assess concordance and operational performance of two AI-based approaches: an AI-assisted CIS (Cerviray AI ® , AIDOT Inc., Seoul, South Korea) and a hybrid AI-assisted CIS incorporating expert review within a VIA-based workflow. Results: The AI-assisted CIS showed sensitivity of 66.7% (95% CI 20.8– 93.9) and specificity of 95.6% (95% CI 87.8– 98.5), with PPV 40.0% and NPV 98.5%. The hybrid approach demonstrated similar sensitivity (66.7%; 95% CI 20.8– 93.9) and slightly higher specificity (97.1%; 95% CI 89.9– 99.2), with PPV 50.0% and NPV 98.5%. VIA positivity was low (4.2%; 8.5% using a composite definition), likely inflating specificity and NPV. All metrics were calculated against VIA rather than histopathology. Agreement with VIA was moderate (κ = 0.472 and κ = 0.550). Conclusion: AI-assisted and hybrid CIS showed moderate agreement with VIA. Interpretation is limited by small sample size, wide confidence intervals for sensitivity, low disease prevalence, and use of VIA as the reference standard. These preliminary findings suggest a potential supportive role for AI-assisted imaging in LMIC screening, warranting validation in larger studies using histopathologic reference standards. The graphical abstract is organized into several panels. One panel states that cervical cancer screening in low- and middle-income countries commonly relies on visual inspection with acetic acid (VIA), which is examiner-dependent and would benefit from more standardized, scalable support. Another panel shows a community-based implementation study in West Bandung, Indonesia, in which 71 women underwent screening. VIA was used as the operational comparator, and digital cervical imaging was evaluated using two approaches: a fully automated AI-assisted cervical imaging system and a hybrid AI-assisted system combining AI output with expert review. A results panel indicates moderate agreement with VIA, with kappa values of 0.47 for AI-assisted CIS and 0.55 for hybrid AI-assisted CIS, high specificity of about 96% to 97%, and exploratory sensitivity estimates with wide uncertainty. Additional panels emphasize that image quality and acquisition affect results, that AI-assisted imaging may support task shifting to trained providers, and that larger multicenter studies with histopathologic validation are needed.Graphical abstract summarizing a community-based study of AI-assisted cervical precancer screening in West Bandung, Indonesia. Keywords: cervical cancer screening, artificial intelligence, comparative evaluation, visual inspection with acetic acid, cervical imaging system