Machine Learning and Artificial Intelligence in the Clinical Microbiology Laboratory (JCM ed.)

The idea of applying machine learning and digital pathology platforms to everyday workflows in the clinical microbiology laboratory has become increasing intriguing and appealing, especially as labs continue to optimize efficiency in the midst of workforce shortages. The promise of this new digital frontier is multifold, including decreasing turnaround time and potentially cost, and freeing up technologist time to focus on higher yield activities in the lab. Many labs have now taken the initial leap into automated culture and imaging systems, but what's next? Are the digital pathology AI algorithms ready for prime-time in clinical microbiology labs? Is the future now? Guests: Dr. Niaz Banaei, Medical Director of the Stanford Health Care Clinical Microbiology Laboratory and Professor of Pathology and Medicine at Stanford University Dr. Dan Rhoads, Section Head of Microbiology at Cleveland Clinic This episode of Editors in Conversation is brought to you by the Journal of Clinical Microbiology and hosted by JCM Editor in Chief, Alex McAdam and Dr. Elli Theel. JCM is available at https://jcm.asm.org and on https://twitter.com/JClinMicro. Links/Refences: Evaluation of MetaSystems automated fluorescent microscopy system for the machine-assisted detection of acid-fast bacilli in clinical samples. https://journals.asm.org/doi/10.1128/jcm.01131-22 Computer vision and artifical intelligence are emerging diagnostic tools for the clinical microbiologist. https://journals.asm.org/doi/10.1128/JCM.00511-20?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed Visit journals.asm.org/journal/jcm to read articles and/or submit a manuscript. Follow JCM on Twitter via @JClinMicro