Volume 23 Issue 9
Sep.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(9): 1535-1538
FAN Aiqing, WANG Hongxing, LI Xiaopan, CHEN Guangpeng, LI Dong, JIN Taotao, HOU Jin. Application effectiveness of AI-assisted diagnosis and treatment systems in general practice clinics[J]. Chinese Journal of General Practice, 2025, 23(9): 1535-1538. doi: 10.16766/j.cnki.issn.1674-4152.004172
Citation: FAN Aiqing, WANG Hongxing, LI Xiaopan, CHEN Guangpeng, LI Dong, JIN Taotao, HOU Jin. Application effectiveness of AI-assisted diagnosis and treatment systems in general practice clinics[J]. Chinese Journal of General Practice, 2025, 23(9): 1535-1538. doi: 10.16766/j.cnki.issn.1674-4152.004172
shu

Application effectiveness of AI-assisted diagnosis and treatment systems in general practice clinics

doi: 10.16766/j.cnki.issn.1674-4152.004172
  • 1.

    Sunqiao Communtiy Health Service Center of Pudong New District, Shanghai 201210, China

Funds:

 PWYq2025-03

 2021-24

  • Received Date: 2025-04-21
    Available Online: 2025-11-17
    Abstract
  •   Objective   To explore the application of an artificial intelligence (AI)-assisted diagnostic system in primary care settings and evaluate its effectiveness in improving disease differentiation efficiency, optimizing medical history documentation, and enhancing the quality of primary healthcare services, thereby providing empirical support for intelligent transformation in grassroots medical practices.   Methods   This empirical study deployed the Ada AI-assisted diagnostic system in two community health centers in Pudong New District, Shanghai from June to September 2024. Utilizing structured intelligent inquiry, differential diagnosis recommendations, and automated medical history generation, data from 987 patients were analyzed to assess system utilization rates, symptom distribution, diagnostic accuracy, and workflow optimization outcomes.   Results   Among 987 patients, the main symptoms included abdominal pain in 112 cases (11.35%), cough in 104 cases (10.54%), and headache in 86 cases (8.71%). The Ada artificial intelligence-assisted diagnosis and treatment system primarily identified hypertension (46 cases, 4.66%), tension-type headache (45 cases, 4.56%), and common cold (42 cases, 4.26%) as the top-ranked diagnoses (M1), while providing 3-5 differential diagnostic suggestions.   Conclusion   The AI system significantly enhances primary screening efficiency in general practice clinics. Through structured data integration, the Ada system improves disease screening efficacy and diagnostic standardization at the primary care level. We propose promoting an "AI-PCP (Primary Care Physician) synergistic model" to strengthen chronic disease and multi-morbidity management capabilities, providing technical support for hierarchical medical systems.

     

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