Applications of machine learning in routine laboratory medicine: Current state and future directions

• Published in: Clinical biochemistry Vol. 103; pp. 1 - 7
• Main Authors: Rabbani, Naveed, Kim, Grace Y.E., Suarez, Carlos J., Chen, Jonathan H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2022
• Subjects: Artificial Intelligence; Biochemistry; Clinical decision support; Clinical pathology; Forecasting; Humans; Laboratories; Machine Learning; Precision Medicine; Biochemistry; Clinical decision support; Precision medicine; Artificial intelligence; Clinical pathology
• ISSN: 0009-9120; 1873-2933; 1873-2933
• DOI: 10.1016/j.clinbiochem.2022.02.011

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.