Rigorous validation of machine learning in laboratory medicine: guidance toward quality improvement

The application of artificial intelligence (AI) in laboratory medicine will revolutionize predictive modeling using clinical laboratory information. Machine learning (ML), a sub-discipline of AI, involves fitting algorithms to datasets and is broadly used for data-driven predictive modeling in various disciplines. The majority of ML studies reported in systematic reviews lack key aspects of quality assurance. In clinical laboratory medicine, it is important to consider how differences in analytical methodologies, assay calibration, harmonization, pre-analytical errors, interferences, and physiological factors affecting measured analyte concentrations may also affect the downstream robustness and reliability of ML models. In this article, we address the need for quality improvement and proper validation of ML classification models, with the goal of bringing attention to key concepts pertinent to researchers, manuscript reviewers, and journal editors within the field of pathology and laboratory medicine. Several existing predictive modeling guidelines and recommendations can be readily adapted to the development of ML models in laboratory medicine. We summarize a basic overview of ML and key points from current guidelines including advantages and pitfalls of applied ML. In addition, we draw a parallel between validation of clinical assays and ML models in the context of current regulatory frameworks. The importance of classification performance metrics, model explainability, and data quality along with recommendations for strengthening journal submission requirements are also discussed. Although the focus of this article is on the application of ML in laboratory medicine, many of these concepts extend into other areas of medicine and biomedical science as well.