ArticleJournal of clinical microbiology2026
What's not to learn? AI meets parasitology.
Article in Journal of clinical microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Performance of the Techcyte AI-based image analysis system for coproparasitological diagnosis in clinical stool specimens: a retrospective evaluation study.Journal of clinical microbiology · 2026Article
Corrections and comments
- Comment on
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.