Machine Learning in Routine Laboratory Medicine: Automation, Utilisation, and Personalised Reference Ranges
This review from Clinical Biochemistry provides a comprehensive overview of machine learning applications in routine clinical laboratory practice, covering test automation, utilisation optimisation, and generation of personalised reference ranges. The authors envision a future laboratory where ML algorithms improve efficiency and diagnostic precision, while noting that few tools have yet been fully implemented due to infrastructure and validation challenges.
The original study
Applications of machine learning in routine laboratory medicine: Current state and future directions.
- Authors
- Rabbani N, Kim GYE, Suarez CJ, Chen JH
- Journal
- Clinical biochemistry
- Type
- Journal Article, Review
- PMID
- 35227670
Original abstract
Machine learning is able to leverage large amounts of data to infer complex patterns that are otherwise beyond the capabilities of rule-based systems and human experts. Its application to laboratory medicine is particularly exciting, as laboratory testing provides much of the foundation for clinical decision making. In this article, we provide a brief introduction to machine learning for the medical professional in addition to a comprehensive literature review outlining the current state of machine learning as it has been applied to routine laboratory medicine. Although still in its early stages, machine learning has been used to automate laboratory tasks, optimize utilization, and provide personalized reference ranges and test interpretation. The published literature leads us to believe that machine learning will be an area of increasing importance for the laboratory practitioner. We envision the laboratory of the future will utilize these methods to make significant improvements in efficiency and diagnostic precision.