应用人工智能和指导方针来帮助医学学生识别心力衰竭患者:随机试验的协议
Hyeon Joo1, Michael R Mathis2, Marty Tam3
1Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, United States.
JMIR research protocols
|October 24, 2023
概括
这项研究评估了人工智能 (AI) 和心力衰竭 (HF) 的指导方针,以改善医学学生的手术前HF识别. 人工智能和指南干预提高了诊断技能,这对于安全的患者护理至关重要.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 心脏病学 心脏病学
背景情况:
- 人工智能 (AI) 在医疗保健中的整合增强了临床任务,诊断准确性和治疗.
- 负责任的AI使用对于减轻偏见和确保安全的临床实践至关重要.
- 由于微妙的症状和诊断局限性,心力衰竭 (HF) 检测手术前是具有挑战性的,这使得人工智能增强的医疗教育决策是一个研究不足的领域.
研究的目的:
- 测量改善手术前HF识别的干预措施的有效性.
- 评估不准确的AI建议对临床决策的影响.
- 探索人工智能推接受度和准确度之间的关系.
主要方法:
- 一个随机试验与医学学生使用3 × 2因数设计.
- 参与者完成了电子学习模块,并审查了20个手术病例,以识别HF.
- 干预措施包括直接的AI建议,间接的AI指标和基于HF指南的教育.
主要成果:
- 截至2023年7月,62名医学学生完成了学习要求,目标是75名三年级和四年级学生.
- 研究协议促进了随机试验的招生和数据收集过程.
- 数据收集正在进行中,招生将于2023年12月结束.
结论:
- 为一项随机试验建立了一个研究协议,评估AI和HF指南干预措施.
- 这项研究旨在通过使用电子健康记录数据,提高医学生在手术前护理中对高频率的认识.
- 该研究协议为评估人工智能在医疗教育中的作用提供了一个框架,用于关键诊断.
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