The need to separate the wheat from the chaff in medical informatics Introducing a comprehensive checklist for the (self)-assessment of medical AI studies

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.

Automated summary

The editorial proposes a practical checklist to help authors self-assess the quality of their contributions and aid reviewers in distinguishing high-quality medical ML studies from the mere application of ML techniques to medical data.