# What’s not to learn? AI meets parasitology

## TL;DR

This paper discusses how AI, specifically a trained CNN, can accurately analyze parasitology smears better than human experts, offering a new tool for clinical microbiology.

## Contribution

The study introduces a CNN with accuracy surpassing trained technologists for analyzing wet-mount parasitology smears.

## Key findings

- The CNN achieved higher accuracy and analytical sensitivity than medical technologists in reviewing parasitology smears.
- The model was trained using a globally sourced dataset, enabling its robust performance.
- The results provide a proof-of-concept for integrating AI into clinical microbiology workflows.

## Abstract

Although artificial intelligence—particularly large-language models—receives daily attention, the application of AI to image-recognition challenges in clinical microbiology has been under development for several years. In the accompanying article, B. A. Mathison, K. Knight, J. Potts, B. Black, et al. (J Clin Microbiol 63:e01062-25, 2025, https://doi.org/10.1128/jcm.01062-25) (in collaboration with ARUP Laboratories and TechCyte) describe a trained convolutional neural network (CNN) that reviews wet-mount parasitology smears with accuracy and analytical sensitivity exceeding that of a cohort of highly trained medical technologists. The impressive results were enabled by an extensive, globally sourced training set. These findings constitute Part II of the authors’ earlier Journal of Clinical Microbiology publication on CNN-based diagnosis of trichrome-stained smears and provide a robust proof-of-concept for integrating AI into clinical microbiology workflows. We comment on the translatability of this technology to routine clinical laboratories.