The potential and prospects of AI empowering grassroots doctors in case analysis and reporting

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.

Key Words: artificial intelligence; general practitioners; case analysis reporting; large language models; medical education; real-world research