Checklist for Medical Imaging using Artificial Intelligence by Evaluation of Machine Learning Models

Enhancing Machine Learning (ML)-based methods that resolve unaddressed medical demands necessitates particular concerns for optimum clinical utility. Latest discussions about the visibility, explainability, and repeatability of ML approaches outlined in this study prompted questions about their clinical utility and appropriateness for inclusion in the present frameworks of evidence-based practice. This highlighted piece emphasizes raising clinicians' ML literacy by arming them with the information and resources required to comprehend and objectively assess the clinical studies encompassing ML. Data collection, feature extraction, model specification, and clinical implementation are the four ML building elements evaluated for rigor and repeatability using a checklist. Such checklists are crucial for ML research to be meticulously and firmly evaluated by clinicians who are informed by the domain expertise of the context in which the results will be implemented. They also help to guarantee quality assurance.