General practice clinical reasoning is a core competency for general practitioners to deliver comprehensive, continuous, and coordinated medical services. Traditional training models are limited by insufficient typical cases, inadequate general practice-specific training, and uneven distribution of teaching faculty. Artificial intelligence provides a novel pathway for the intelligent transformation of general practice clinical reasoning education.

To develop and validate a systematic AI-enabled clinical reasoning training program tailored to the characteristics of general practice, and to evaluate its implementation effect and acceptance among teachers and trainees in general practice education.

From March to May 2025, a "Problem-Design-Implementation-Evaluation-Reflection" framework will be adopted. Firstly, a diagnostic questionnaire survey will be conducted among 122 general practice clinical instructors and 164 general practice resident physicians undergoing standardized training (hereinafter referred to as general practice residents). Based on this, an AI-integrated training program incorporating general practice characteristics will be designed, comprising four major modules: "General Practice Intelligent Case Deduction - Real-time Feedback and Assessment-Personalized Learning Pathways-General Practice Faculty Guidance." A parallel-group randomized controlled trial (RCT) was performed: 90 general practice residents were randomly assigned 1∶1 to the experimental group (n=45) and the control group (n=45). The experimental group received a 12-week AI-integrated training intervention, including an AI general practice clinical reasoning platform, general practice-specific cases, comorbidity management, family assessment, and community emergency response. The control group received traditional general practice clinical reasoning training (theoretical lectures, case discussions, community clerkships, etc.). Intervention effects were assessed before and after training using general practice theoretical knowledge tests, the General Practice Clinical Reasoning Scale (G-DRS), chronic disease management reasoning scores, referral decision-making ability scores, and a learning satisfaction questionnaire.

The current diagnostic results show that 93.4% (114/122) of teachers and 87.2% (143/164) of students have used AI tools. Both teachers and students hold a positive attitude towards AI empowering the cultivation of clinical thinking in general practice, but only 33.6% (41/122) believe that existing tools meet the needs for general practice thinking cultivation. Teachers and students primarily focus on issues such as the authenticity of general practice-specific cases [61.5% (75/122)], simulation of grassroots referral scenarios [54.9% (67/122)], the risk of clinical thinking rigidity due to over-reliance on AI technology [79.9% (131/164)], and feedback lacking a humanistic dimension [56.6% (69/122)]. After group training, the experimental group's G-DRS score (27.9±3.5), chronic disease management thinking score (86.2±8.3), and referral decision-making ability score (85.7±7.9) were all higher than those of the control group [(22.5±4.1), (80.1±9.6), and (79.3±8.8), respectively] (P<0.05). After the intervention, the increase in the G-DRS score was greater in the experimental group than in the control group (P<0.05). The popularity of each teaching module was, in descending order: intelligent case deduction in general practice [95.3% (41/43)], general practice faculty guidance and workshops [93.0% (40/43)], real-time feedback and evaluation [88.4% (38/43)], and personalized learning paths [79.1% (34/43)].

General practice teachers and residents are open to AI-enabled clinical reasoning training, with key concerns focusing on case authenticity, scenario simulation, and humanistic feedback. The AI-enabled training program can effectively improve the clinical reasoning ability of general practice trainees. It provides replicable and scalable empirical evidence for the intelligent development of general practice education in China.