This editorial aims to contribute to the current debate about the quality of studies that apply machine learning (ML) methodologies to medical data to extract value from them and provide clinicians with viable and useful tools supporting everyday care practices. We propose a practical checklist to help authors to self assess the quality of their contribution and to help reviewers to recognize and appreciate high-quality medical ML studies by distinguishing them from the mere application of ML techniques to medical data.

中文翻译：

---

需要在医学信息学中将小麦与谷壳分开

这篇社论旨在促进当前关于将机器学习 (ML) 方法应用于医疗数据以从中提取价值并为临床医生提供支持日常护理实践的可行且有用的工具的研究质量的辩论。我们提出了一个实用的清单，以帮助作者自我评估其贡献的质量，并通过将高质量医学 ML 研究与仅将 ML 技术应用于医学数据区分开来，帮助审阅者识别和欣赏高质量的医学 ML 研究。