Volume 29 Issue 10
Oct.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(10): 1786-1789
JIN Haohan, ZHAI Wenqing, JIN Shenying, JIANG Juan, MA Yajun. Prospective applications of artificial intelligence in general practice: clinical decision-making, patient communication, and residency education[J]. Chinese Journal of General Practice, 2025, 23(10): 1786-1789. doi: 10.16766/j.cnki.issn.1674-4152.004229
Citation: JIN Haohan, ZHAI Wenqing, JIN Shenying, JIANG Juan, MA Yajun. Prospective applications of artificial intelligence in general practice: clinical decision-making, patient communication, and residency education[J]. Chinese Journal of General Practice, 2025, 23(10): 1786-1789. doi: 10.16766/j.cnki.issn.1674-4152.004229
shu

Prospective applications of artificial intelligence in general practice: clinical decision-making, patient communication, and residency education

doi: 10.16766/j.cnki.issn.1674-4152.004229
  • 1.

    Department of General Practice, Lukou Community Health Service Center, Jiangning District, Nanjing, Jiangsu 210000, China

Funds:

 Z2021092

  • Received Date: 2025-03-16
    Abstract
  •   Objective  To evaluate the application prospects of artificial intelligence (AI) in general practice and explore its potential as both a practical tool for addressing common patient inquiries and a clinical teaching resource.  Methods  Twenty standardized clinical queries frequently encountered in general practice were systematically selected. Responses were independently generated by AI systems [ChatGPT (GPT) and DeepSeek (DS)] and human participants comprising first-year residents (Y1), third-year residents (Y3), and attending physicians specialized in general practice. Responses were evaluated across five domains using a 5-point Likert scale: accuracy, comprehensiveness, detail, patient-centered communication, and continuity of care management. Statistical analyses included one-way ANOVA and Tukey's HSD post hoc tests.  Results  GPT and DS demonstrated significantly superior performance to all physician groups in accuracy, comprehensiveness, and detail. In patient-centered communication, AI performance (GPT: 3.16±0.42; DS: 3.23±0.09) exceeded that of Y1 residents (2.33±0.45), was comparable to Y3 residents (3.76±0.27), but remained significantly inferior to attending physicians (4.26±0.27). Performance in continuity of care management followed an analogous pattern.  Conclusion  AI exhibits significant potential for implementation in general practice, particularly for delivering accurate, comprehensive, and intelligible responses to standardized clinical queries, highlighting its significant application potential as a foundational tool and a knowledge aid for resident trainees. However, limitations persist in domains requiring experiential judgment(e.g., patient-centered communication, continuity of care), where AI underperforms relative to senior clinicians. Future development should focus on enhancing AI's contextual adaptability in complex scenarios. Within residency training frameworks, AI tools may effectively serve as knowledge consolidation aids, yet cultivating advanced communication skills and empathetic patient engagement necessitates direct clinical mentoring and iterative practice under physician supervision.

     

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