Factors Influencing Universal Coverage of AI-Assisted Cervical Cancer Screening: Qualitative Study Based on the Macro Model of Health System

Background: Improving screening coverage is a central goal of the global strategy to eliminate cervical cancer. In resource-constrained settings, insufficient service accessibility remains a key barrier to expanding coverage. Supported by artificial intelligence (AI)–assisted diagnostic technology, Hubei province has pioneered China’s first provincial-level population-wide cervical cancer screening program, serving 12.67 million eligible women. This initiative provides an innovative practice for addressing such challenges. Objective: This study systematically examines major factors influencing the achievement of universal screening coverage targets through interviews with core managers and implementers of Hubei province’s screening program. It aims to provide empirical evidence and strategic recommendations for applying AI technologies in cervical cancer screening and enhancing screening coverage rates. Methods: The interview guide was developed under the guidance of the macro model of health system. A combination of purposive sampling and multistage stratified sampling was used to capture provincial-level overviews and understand regional implementation variations, respectively. Guided by the macro model of health system, interview outlines were developed. Semistructured interviews were conducted between January and August 2024 with key project personnel (one per institution) from 14 relevant institutions. Interview data were analyzed using thematic analysis, with systematic coding and management facilitated by the NVivo software. Results: Key informants reported that comprehensive screening has been largely achieved. The analysis identified government stewardship, AI-assisted screening technology, screening funding, and health literacy as the major factors for achieving universal screening coverage. Among these, government leadership and the application of AI-assisted diagnostic technologies provide significant driving factors. Additional factors encompassed structural dimensions, including multisectoral coordination, trained screening technicians, and information systems; process dimensions, such as institutional service delivery capacity, quality control measures, and community mobilization; along with outcome dimensions comprising population coverage, cytology positivity rate, follow-up, and treatment rate. Conclusions: Achieving large-scale cervical cancer screening requires coordinated efforts across four dimensions: government stewardship, screening technology, screening funding, and health literacy. Government stewardship served as the core driver in advancing population-wide screening coverage. Its mechanisms included coordinated procurement of AI-assisted screening services, secured financial investment, formulation of targeted policies, promotion of multi-sectoral collaboration, and optimization of service delivery models. These efforts systematically improved the accessibility and utilization of screening services, ultimately encouraging and facilitating active participation among residents.