关于儿童糖尿病的机器学习研究报告质量:系统审查
Zsombor Zrubka1, Gábor Kertész2, László Gulácsi1
1HECON Health Economics Research Center, University Research and Innovation Center, Óbuda University, Budapest, Hungary.
Journal of medical Internet research
|January 19, 2024
概括
儿童糖尿病 (DM) 机器学习 (ML) 研究报告质量较低. 临床医生的关键细节,如患者特征和模型检查,经常缺失,阻碍了透明度和可重复性.
科学领域:
- 医疗人工智能 医疗人工智能
- 儿科内分泌学 儿科内分泌学
- 临床信息学 临床信息学
背景情况:
- 糖尿病 (DM) 在儿童中构成了重大的健康挑战,技术进步使其变得更加严重.
- 人工智能 (AI) 研究在医学上的透明度,可复制性和有效性越来越令人担忧,特别是在儿科.
- 在医疗AI中需要标准化的报告准则,这对于确保研究严格性和临床适用性至关重要.
结论:
- 在儿科糖尿病中进行的机器学习研究通常存在报告标准不佳的问题.
- 临床决策的基本信息,包括患者人口统计和模型验证,往往是不充分的.
- 提高透明度和遵守MI-CLAIM等报告准则对于推动人工智能在儿科DM中的临床实用性至关重要.
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