Details

Autor(en) / Beteiligte

• Tran, Nam K
• Albahra, Samer
• May, Larissa
• Waldman, Sarah
• Crabtree, Scott
• Bainbridge, Scott
• Rashidi, Hooman

Titel

Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing

Ist Teil von

• Clinical chemistry (Baltimore, Md.), 2022-01, Vol.68 (1), p.125-133

Ort / Verlag

England: Oxford University Press

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Oxford Journals 2020 Medicine

Beschreibungen/Notizen

• 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.