Fine-grained medical operations are a core driver of high-quality hospital development, essentially breaking away from traditional, inefficient management models to achieve precise governance across medical resources, service processes, cost control, and quality supervision. Leveraging the advantages of artificial intelligence—such as data mining, intelligent analysis, and autonomous iteration—hospitals can address issues like low operational efficiency, misallocation of resources, and outdated management practices. This paper explores the relationship between AI and fine-grained hospital operations, analyzing how their respective strengths can be integrated. It examines practical applications and implementation pathways through four dimensions: clinical service refinement, resource allocation optimization, cost control precision, and quality oversight. The study identifies existing challenges in current integration efforts, including inadequate technical compatibility, incomplete data systems, severe talent shortages, and insufficient regulatory frameworks. It discusses solutions and proposes improvement directions to promote public hospitals toward more refined, intelligent, and standardized operations, thereby advancing the healthcare industry to higher standards.