AI and Machine Learning Are Reshaping Infectious Disease Diagnostics
This review maps the current and emerging applications of artificial intelligence and machine learning in infectious disease testing, covering COVID-19, sepsis, hepatitis, malaria, and tuberculosis. The authors introduce the concept of 'data fusion,' where multiple laboratory data streams are integrated by ML algorithms to generate actionable clinical insights. As commercially available AI-powered platforms enter the market, laboratory professionals need to understand these tools to evaluate and implement them effectively.
The original study
Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing.
- Authors
- Tran NK, Albahra S, May L, Waldman S, Crabtree S, Bainbridge S, et al.
- Journal
- Clinical chemistry
- Type
- Journal Article, Review
- PMID
- 34969102
Original abstract
BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available. CONTENT: In this review we: (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease testing, and (d) discuss the future evolution AI/ML for infectious disease testing in both laboratory and point-of-care applications. SUMMARY: The review provides an important educational overview of AI/ML technique in the context of infectious disease testing. This includes supervised ML approaches, which are frequently used in laboratory medicine applications including infectious diseases, such as COVID-19, sepsis, hepatitis, malaria, meningitis, Lyme disease, and tuberculosis. We also apply the concept of "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.