病例分析报导是基层医生将临床实践、诊疗思维、随访结果与经验反思转化为可传播知识的关键载体，具有鲜明的医疗、教学与科研三重属性。高质量病例分析报导不仅能够提高基层首诊识别、转诊决策和慢病连续管理水平，而且有助于沉淀可复盘、可教学、可研究的一线经验。当前，基层医生在病例分析报导实践中普遍面临时间不足、资料碎片化、临床推理链条显化不充分、规范化写作能力不足、文献检索效率不高以及科研转化门槛较高等现实障碍。近年来，生成式人工智能、大语言模型、自然语言处理、多模态模型、环境语音记录、知识图谱，以及物联网和元宇宙医学等技术快速演进，为基层病例分析报导提供了从资料采集到知识转化的全流程赋能路径。AI可在病史采集与结构化整理、病例时间线重建、问题表征、鉴别诊断提示、循证证据辅助、病例教学设计、病例库建设与科研转化等方面发挥作用，从而提升病例分析报导的完整性、规范性、可解释性与复用价值。现有研究表明，AI在复杂诊断推理、病例文本生成、医学教育和临床文书辅助等方面已展现出可观潜力，但其在基层真实世界场景中的应用仍受到幻觉、偏倚、隐私保护、责任归属、外部可迁移性不足以及能力受限等问题制约。未来，应坚持“人机协作、医生主导、事实可核、流程可审计、场景渐进部署”的原则，构建面向基层的智能病例生态，将病例分析报导升级为集诊疗、教学、科研、质控与区域知识共享于一体的数字化能力体系。

Case analysis reporting is an important approach for general practitioners to transform clinical practice, diagnostic reasoning, follow-up observations, and reflective learning into sharable medical knowledge, with substantial clinical, educational, and research value. High-quality case analysis reporting can improve first-contact recognition, referral decisions, chronic disease management, and regional quality improvement, while also serving as an effective vehicle for case-based teaching, young physician training, and real-world evidence generation. However, in routine practice, general practitioners often face multiple barriers, including limited consultation time, incomplete data collection, weak diagnostic reasoning frameworks, insufficient standardized writing skills, difficulty in evidence retrieval, and low research conversion efficiency. Recent advances in generative artificial intelligence, large language models, natural language processing, multimodal AI, ambient clinical documentation tools, knowledge graphs, Internet of Things, and metaverse medicine have created new opportunities for empowering case analysis reporting in primary care. AI can support history taking, structured data extraction, reconstruction of disease timelines, problem representation, differential diagnosis prompting, evidence retrieval, case-based educational design, case repository development, and research transformation, thereby improving the completeness, standardization, interpretability, and reusability of case reports. Current studies suggest that AI has shown promising performance in complex diagnostic reasoning, clinical text generation, medical education, and documentation assistance. Nevertheless, real-world implementation in primary care remains constrained by hallucinations, bias, privacy risks, unclear accountability, limited external generalizability, and the potential erosion of clinicians’ independent reasoning ability. Looking forward, AI empowerment in primary care case analysis reporting should follow the principles of human-AI collaboration, physician leadership, factual verifiability, auditability, and gradual scenario-based deployment. The ultimate goal is not merely to help physicians write faster, but to build an intelligent case ecosystem that integrates clinical care, education, research, quality assurance, and regional knowledge sharing.