Artificial Intelligence for Cervical HPV Infection and Lesion Screening: A Cross-Sectional Analysis of Its Application Potential and Patient Satisfaction.

OBJECTIVE: This cross-sectional study aimed to explore the application potential of artificial intelligence (AI) in screening and diagnosing cervical human papillomavirus (HPV) infection and lesions, and to assess patient satisfaction with the current diagnostic and therapeutic process as well as their unmet needs. METHODS: An online cross-sectional survey was conducted via the Questionnaire Star platform, and 308 valid responses were collected. Descriptive statistics were used to summarize participants' demographic characteristics and questionnaire responses. Chi-square tests were performed to examine associations between demographic factors (e.g., age, residence) and key outcomes (e.g., AI acceptance, primary concerns). A two-tailed P-value < 0.05 was considered statistically significant. Qualitative content analysis was applied to synthesize and interpret responses to open-ended questions. RESULTS: Most respondents were women aged 25-35 years (34.74%), with 84.74% residing in urban areas. Among all participants, 76.30% reported a history of HPV infection, and 91.56% had undergone ThinPrep cytologic test (TCT). The most distressing part of the screening process was anxiety during result waiting (41.23%), and 58.12% found medical terminology difficult to understand. Although 61.04% of respondents had no prior knowledge of AI-assisted diagnosis, 58.77% were willing to learn about its application in improving diagnostic efficiency. Younger respondents (≤35 years) showed significantly higher willingness to learn about AI than those aged >35 years (65.1% vs. 52.4%, χ²=6.24, P=0.012). Additionally, 75.97% of respondents believed AI could shorten result waiting times, and 73.70% trusted the "AI preliminary screening + physician confirmation" model. The top concerns regarding AI application were technical reliability (70.78%) and data privacy (68.18%). CONCLUSION: Patients with cervical lesions have strong demands for diagnostic efficiency, psychological support, and information transparency. AI technology holds great potential in enhancing screening efficiency and assisting diagnosis; however, key challenges remain, including ensuring data privacy, improving technical reliability, and strengthening patient trust.