Applications for AI in the Clinical Parasitology Lab
The focus of this presentation is on the implementation of digital microscopy and artificial intelligence (AI) in the clinical parasitology laboratory. After a brief review on the morphologic identification of intestinal parasites the potential benefits of adopting AI for screening stool specimens processed for parasitology will be discussed. Potential workflow changes labs may encounter, such as changes to specimen preparation, reorganization of workforce members, and how to handle quality control, competency, and proficiency will be reviewed. Lastly, a couple case examples of image analysis with a discussion of our lab’s workflow and how we handle discrepant analysis will be provided.
Originally published on February 4, 2026
Lecture Presenter
![]() | Blaine A. Mathison, BS, M(ASCP) Adjunct Instructor, Department of Pathology |
Blaine Mathison is currently the technical director of parasitology at ARUP Laboratories in Salt Lake City and an adjunct instructor for the Department of Pathology at the University of Utah. Mathison has been doing diagnostic parasitology for 25 years, including at the Arizona State Public Health Laboratory, the Phoenix Veterans Administration, and the Division of Parasitic Diseases and Malaria at the Centers for Disease Control and Prevention. He publishes and lectures regularly on a variety of topics related to both parasitology and entomology, including taxonomy, biology, diagnostics, pathology, and case reports. Mathison’s specialties include unusual and zoonotic helminth infections, histopathology of parasitic infections, and arthropods of medical importance. In 2018, he was the recipient of the ASM Scherago-Rubin Award, which recognizes contributions by nondoctoral-level microbiologists to the field of clinical microbiology.
Objectives
After this presentation, participants will be able to:
- Review the morphologic identification of intestinal parasites
- Describe potential workflow changes when adopting AI for parasitology
- Discuss proficiency, competency, and quality control as it pertains to AI
Sponsored by:
University of Utah School of Medicine, Department of Pathology, and ARUP Laboratories


