Applications of Artificial Intelligence in Clinical Microbiology Diagnostic Testing

Kenneth P. Smith, Hannah Wang, Thomas J.S. Durant, Blaine A. Mathison, Susan E. Sharp, James E. Kirby, S. Wesley Long, Daniel D. Rhoads

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

The use of artificial intelligence (AI) computer software to interpret data has become part of our everyday lives, and these AI algorithms are becoming part of our everyday laboratory practices. Many AI tools are beginning to demonstrate their real or potential utility in clinical microbiology laboratory practice. In this introduction to applications of AI in clinical microbiology diagnostic testing, the authors introduce AI and machine learning to those familiar with routine clinical microbiology practice. The discussion explores the role of AI for image analysis including Gram stains, ova and parasite exam, and digital plate reading of bacterial cultures. AI's role in advanced analysis of matrix-assisted laser desorption-ionization/time of flight mass spectrometry (MALDI-TOF) mass spectral data and whole genome sequence data of microbes is also discussed. In the future, computers and clinical laboratory scientists will work more closely together to provide optimal efficiency and quality in clinical microbiology laboratory practice, and this close collaboration between humans and machines is expected to improve patient care.

Original languageEnglish (US)
Pages (from-to)61-70
Number of pages10
JournalClinical Microbiology Newsletter
Volume42
Issue number8
DOIs
StatePublished - Apr 15 2020

ASJC Scopus subject areas

  • Microbiology (medical)
  • Infectious Diseases

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