在出血性中风人工智能研究中需要透明度和临床解释性:促进有效的临床应用
Chae Young Lim1, Beomseok Sohn1, Minjung Seong1
1Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Yonsei medical journal
|September 23, 2024
概括
人工智能 (AI) 和机器学习 (ML) 关于出血性中风的研究报告质量不理想. 提高透明度和可解释性对于这些AI/ML工具的临床应用至关重要.
科学领域:
- 医疗人工智能 医疗人工智能
- 机器学习在神经病学中的应用
- 临床信息学 临床信息学
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在诊断和管理出血性中风方面表现有前途.
- 标准化的报告框架对于确保AI/ML医学研究的质量和可重复性至关重要.
研究的目的:
- 评估AI/ML关于出血性中风的研究报告质量.
- 评估遵守医学人工智能报告 (MINIMAR) 和临床人工智能建模 (MI-CLAIM) 框架的最低信息.
- 确定需要改进的领域,以促进AI/ML在出血性中风中的临床应用.
主要方法:
- 在PubMed,MEDLINE和Embase进行了系统的文献搜索.
- 包括29项有关血液性中风的AI/ML研究.
- 两位经验丰富的放射科医生使用MINIMAR和MI-CLAIM框架评估了研究质量.
主要成果:
- 对MINIMAR和MI-CLAIM的坚持率很低 (分别为47.6%和46.0%).
- 报告的主要遗漏包括患者的社会经济地位,缺少数据的处理和模型解释能力技术.
- 观察到的透明度有限,只有10.3%的研究共享他们的代码.
结论:
- 已发表的AI/ML关于出血性中风的研究显示报告质量不足于最佳.
- 纳入模型检查技术和促进代码共享对于提高透明度和可重复性至关重要.
- 改进报告标准将提高AI/ML工具在治疗出血性中风中的临床适用性.
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