Artificial Intelligence (AI) is a crucial component of China's new-generation information technology development strategy. With its extensive applications in the healthcare field, the integration of AI into general practice for auxiliary diagnosis and chronic disease management plays a significant role in enhancing primary healthcare services and improving the professional skills and clinical decision-making capabilities of general practitioners.

Through a preliminary investigation into general practitioners' cognition of AI technology in the medical field and their needs for constructing auxiliary diagnosis and treatment systems across various provinces and cities, this study aims to provide references for establishing more clinically applicable AI-assisted diagnosis and treatment systems.

From March to April 2024, we selected general practitioners (GPs) from across China's provinces and municipalities as study participants through national GP WeChat groups. Using a self-designed questionnaire, we collected data on: GPs' basic demographic information; their attitudes toward and understanding of AI-assisted diagnosis and treatment systems; and their needs regarding AI applications in daily clinical practice (including clinical consultations, auxiliary diagnosis and treatment, referral and follow-up, and application formats) . Data collection was conducted via the"Questionnaire Star" platform, and descriptive analysis methods were employed to present and characterize the results.

This study enrolled 382 general practitioners (GPs) from 27 provinces across China, comprising 52.36% (200/382) from general hospitals and 47.64% (182/382) from primary healthcare institutions. The survey revealed that 57.69% (220/382) of respondents believed physicians and AI should work synergistically, while 77.75% (297/382) of practitioners expressed willingness to adopt or continue using AI-assisted diagnostic technologies in clinical practice; The survey identified key advantages of AI adoption, including improved diagnostic efficiency [89.01% (340/382) ] , reduced clinical workload [84.29% (322/382) ] , and decreased misdiagnosis rates 80.10% (306/382) . However, significant concerns were noted regarding: over-reliance on AI diagnosis [75.13% (287/382) , ethical implications [64.14% (245/382) ] , algorithmic diagnostic bias [63.09% (241/382) ] , data security vulnerabilities [57.33% (219/382) ] , and liability ambiguity in medical incidents [53.66% (205/382) ] ; In the clinical consultation needs assessment, general practitioners most frequently requested AI-enabled alerts for"life-threatening conditions" [61.25% (234/382) ] and prompts for"urgently actionable clinical signs" [62.04% (237/382) ] . Significant demand was also observed for"associated symptom inquiry prompts" [58.90% (225/382) ] and"systematic clinical reasoning guidance" [62.57% (239/382) ] ; In the auxiliary diagnosis and treatment domain, rapid and precise imaging evaluation [67.80% (259/382) ] and prescription review [67.01% (256/382) ] emerged as the most anticipated AI functions. Significant attention was also given to personalized medication recommendations [63.88% (244/382) ] and insurance coverage alerts [66.49% (254/382) ] ; Regarding referral and follow-up management, general practitioners showed the strongest demand for three key AI functions: automated reminders for follow-up visit scheduling [72.25% (276/382) ] , remote monitoring capabilities through smart devices [71.46% (273/382) ] and personalized delivery of patient education content [70.42% (269/382) ] ; In terms of system application formats, general practitioners showed the strongest preference for"reviewing patients' historical medical records" [73.04% (279/382) ] and"voice-to-text transcription" [71.73% (274/382) ] ; In terms of priorities for incorporating undifferentiated diseases into AI-assisted diagnostic and treatment systems, 37.43% (143/382) of general practitioners recommended prioritizing the inclusion of fever in AI-assisted diagnosis and treatment systems.

General practitioners demonstrated overall high willingness to adopt AI technology for clinical diagnosis and treatment. The AI-assisted clinical decision support system showed significant demand across multiple clinical workflows, including medical history collection, physical examination, diagnostic testing, treatment planning, referral criteria determination, follow-up management, and system application formats. Moving forward, the development of AI-assisted diagnosis and treatment systems tailored for Chinese general practitioners—through overcoming technological limitations, establishing clear legal frameworks and guidelines for AI, and enhancing GPs' AI literacy—will significantly improve primary care physicians' first-contact diagnostic capabilities and facilitate the implementation of tiered healthcare delivery systems.