与完成评估人工智能的临床试验相关的试验因素:回顾性病例对照研究
David Chen1, Christian Cao1, Robert Kloosterman1
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Journal of medical Internet research
|September 23, 2024
概括
临床试验设计因素,如欧洲位置和更大的样本大小,提高了人工智能 (AI) 工具的成功. 了解这些因素是防止AI临床试验失败的关键.
科学领域:
- 临床研究方法论临床研究方法论
- 医疗保健中的人工智能
- 生物医学信息学是生物医学信息学.
背景情况:
- 在临床试验中评估人工智能 (AI) 工具对于它们在医疗保健中的采用至关重要.
- 然而,影响人工智能试验成功的关键设计因素和失败的常见原因尚未得到充分理解.
研究的目的:
- 为了比较完成和不完整的临床试验评估AI工具之间的试验设计因素.
- 确定与成功完成AI临床试验相关的因素.
主要方法:
- 一项病例控制研究分析了来自ClinicalTrials.gov.gov的485个完整和51个不完整的AI临床试验.
- 试验设计因素,包括临床应用,预期人口,人工智能作用,研究类型,样本大小和地理位置,都被提取和分析.
- 使用倾向匹配的多变量逻辑回归来计算赔率比率 (OR) 和95%置信区间 (CI).
主要成果:
- 与北美试验相比,在欧洲进行的试验显示完成率明显高 (OR 2.85,95% CI 1.14-7.10;P=.03).
- 较大的试验样本大小与试验完成积极相关 (OR 1.00,95% CI 1.00-1.00;P=.02).
结论:
- 这项研究确定了欧洲的位置和更大的样本大小是与AI临床试验完成相关的重要因素.
- 研究结果强调需要解决特定的设计因素,以提高人工智能试验的成功率并减少研究失败.
- 未来的研究应该专注于优化人工智能试验设计,以促进成功转化为临床实践.
关键词:
在这里,我们可以看到AIAIAI.欧洲欧洲欧洲欧洲欧洲欧洲欧洲申请申请表 申请表 申请表人工智能的人工智能是人工智能.临床临床临床临床临床临床临床临床试验临床试验临床试验临床试验临床试验完成完成完成完成完成完成跨截面研究是跨截面研究.医疗保健 医疗保健 医疗保健卫生信息 卫生信息信息学是一个信息学领域.干预干预干预干预干预干预逻辑回归的逻辑回归方法试验设计试验设计.试验 试验 测试 测试更多相关视频
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