Abstract
OBJECTIVE: Research on healthy villages in China is currently constrained by limited evaluation criteria and a lack of systemic comprehensiveness. This study aims to develop a scientifically rigorous evaluation index system that is tailored to the regional characteristics of China. METHOD: A modified Delphi method was employed to screen indicators based on literature review and expert consultation, followed by the Analytic Hierarchy Process (AHP) to determine the weights of these indicators. Innovatively, this study adopted a Human-AI synergistic approach throughout the research lifecycle; generative AI was utilized to refine indicator semantics during the Delphi phase, while an LLM-assisted comparative analysis served as a robustness check for the weighting system. Additionally, empirical validation was conducted in three pilot villages. RESULTS: The final system consists of 7 first-level, 31 s-level, and 61 third-level indicators. Metrics from expert consultations were satisfactory, with authority coefficients exceeding 0.80 and demonstrating strong coordination (P < 0.001). Weight analysis indicated that "Healthy Population" (0.340) and "Healthy Life" (0.228) are the most critical dimensions. The robustness check revealed a strong correlation (Pearson's r = 0.99) between human expert consensus and AI-simulated weights, thereby confirming the system's validity. Furthermore, empirical application in three pilot villages produced distinct scores (97.1, 77.3, and 39.7), which accurately reflected developmental disparities and identified specific weaknesses for targeted intervention. CONCLUSION: The constructed index system integrates multi-dimensional health factors with a scientifically rigorous design validated through this Human-AI synergistic strategy. Ultimately, this approach pioneers new pathways for the deep integration of artificial intelligence and public health management, while providing a reference model for developing comprehensive evaluation systems in other developing countries.