ArticleCytoJournal2026
Automating fine needle aspiration and non-gynecologic quality control using a large language model: A real-world evaluation of diagnostic accuracy and efficiency.
Article in CytoJournal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Cytopathology laboratories are required to track diagnostic category distributions as part of quality control (QC) under Clinical Laboratory Improvement Amendments regulations. This process is typically manual, time-consuming, and prone to inconsistency. We evaluated whether a large language model (LLM) could accurately and efficiently categorize cytopathology reports to support regulatory QC processes. Material and Methods: A total of 472 fine needle aspiration and non-gynecologic (NG) cytology reports from 1-31 January 2024 were processed using DeepSeek-R1:32B, a locally deployed open-source LLM. GYN cytology reports were excluded because their diagnostic categories are already captured as discrete data in our laboratory information system, eliminating the need for natural language processing. The model extracted the final diagnosis, diagnostic category, and confidence score from each report. A board-certified cytopathologist independently reviewed all cases to serve as the ground truth. Cases were categorized as correct or incorrect. Time required for manual versus artificial intelligence (AI)-assisted review was also measured. Results: The model achieved 98.5% categorization agreement (95% confidence interval: 97.0-99.3%; Cohen's Kappa = 0.973) with expert review (465/472 cases were classified as correct), with only 1.5% (7/472) raw misclassifications. Most misclassifications were due to semantic ambiguity, missing context, or institutional interpretation conventions. The pipeline implemented a human-in-the-loop mechanism by flagging low-confidence or non-standard outputs for manual review, enhancing safety. The AI-assisted workflow reduced total review time by 98.5%, from 279 to 4.15 min. Conclusion: This study demonstrates that LLMs can accurately and safely support cytopathology QC tasks, significantly reducing workload while maintaining reporting consistency. With further development and validation, LLM-powered tools may become integral to pathology workflows across specialties.
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.