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通过虚拟的2型糖尿病情景,增强医学学生的诊断沟通的基于人工智能的前后模拟研究
Ricardo Xopan Suárez-García1,2, Quetzal Chavez-Castañeda2, Rodrigo Orrico-Pérez2
1Unidad de Remisión de Diabetes Mellitus (URDM), Facultad de Estudios Superiores-Iztacala, Universidad Nacional Autónoma de México, Tlalnepantla 54090, Mexico.
概括
生成型人工智能 (GenAI) 培训显著提高了医学学生的诊断沟通技能,特别是在解释2型糖尿病诊断方面. 在患者的接触中提高了清晰度,结构和同情心.
科学领域:
- 医学教育
- 医疗保健中的人工智能
- 诊断沟通
背景情况:
- 有效的诊断沟通对于患者的结果至关重要.
- 传统的医疗培训可能无法充分为学生提供必要的沟通技巧.
- 创造性人工智能 (GenAI) 提供了新的培训方式.
研究的目的:
- 评估DIALOGUE (通过客观指导用户体验进行诊断人工智能学习) 计划在提高医学学生诊断沟通技能的有效性.
- 评估GenAI介导培训对学生披露2型糖尿病 (T2DM) 诊断的能力的影响.
- 确定影响诊断沟通改善的因素.
主要方法:
- 一个单臂,前后研究设计,涉及30名临床阶段的医学学生.
- 在试验前和试验后的评估中,使用模拟的患者接触,通过盲目评价者进行评分.
- 使用DIALOGUE程序进行自动反的十个异步GenAI培训场景.
- 分析了八个沟通领域和影响因素的业绩增长.
主要成果:
- 总体诊断沟通性能显著改善 (平均增加36. 7分,p < 0. 001).
- 优秀学生的比例从0%增加到70%.
- 在所有领域都观察到增长,在接触开放,关闭和糖尿病特异性解释方面有显著的改善.
结论:
- 通过GenAI培训,例如DIALOGUE计划,可以大大提高医学学生的诊断沟通技能.
- 该计划显示了传统医学教育的可扩展和个性化补充的潜力.
- 学习者的特点如基线同情和数字自我效能影响了培训结果.
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