基于AI的慢性肾脏病(CKD)早期诊治与临床教学策略研究

Research on AI-Based Early Diagnosis and Treatment Strategies for Chronic Kidney Disease (CKD) and Clinical Teaching Approaches

摘要: 目的：基于AI构建CKD早期诊治模型，辅助教学，评估AI辅助教学对医学生早期肾脏病诊治知识与技能掌握的提升情况。方法：选择我院2022级五年制临床医学院和全科医学院的78名实习生随机分配到两个不同的组别：观察组和对照组，观察组为基于人工智能辅助教学，对照组的教学方式为传统的临床教学模式，教学结束后，比较两组学生的理论考核成绩，并对他们的满意度、主观体验等进行问卷调查。结果：观察组理论知识考核平均成绩27.476分高于对照组25.703分，差异有统计学意义(t = 5.583, P < 0.01)；观察组技能操作平均成绩39.159分高于对照组36.029分，差异有统计学意义(t = 7.977, P < 0.01)；观察组病例分析考核成绩17.706高于对照组16.235，差异有统计学意义(t = 4.853, P < 0.01)。对照组学生实习教学满意度低于观察组，差异有统计学意义(P < 0.01)。结论：基于AI的慢性肾脏病(CKD)早期诊治与临床教学，提高了学生的实习质量和对教学的满意度。

Abstract: Purpose: Developing an AI-based model for the early diagnosis and management of chronic kidney disease (CKD) to support medical education, and evaluating the impact of AI-assisted teaching on enhancing clinicians’ knowledge and skills in managing early-stage kidney disease. Method: Seventy-eight interns from the five-year clinical medicine and general practice programmes of our institution’s 2022 cohort were randomly assigned to two distinct groups: an observation group and a control group. The observation group received AI-assisted teaching, while the control group underwent traditional clinical instruction. Following the teaching period, theoretical examination results were compared between groups, alongside conducting surveys on student satisfaction and subjective experiences. Results: The mean theoretical knowledge assessment score in the observation group (27.476) was significantly higher than that in the control group (25.703) (t = 5.583, P < 0.01). The mean practical skills assessment score in the observation group (39.159) was significantly higher than that in the control group (36.029) (t = 7.977, P < 0.01). The case analysis assessment score of the observation group (17.706) was higher than that of the control group (16.235), with a statistically significant difference (t = 4.853, P < 0.01). The control group students’ satisfaction with practical teaching was lower than that of the observation group, with a statistically significant difference (P < 0.01). Conclusion: AI-based early diagnosis and treatment of chronic kidney disease (CKD) integrated with clinical teaching enhanced the quality of student placements and satisfaction with teaching.