ArticleEndocrine2026
Evaluation of large language models for TI-RADS category extraction from free-text thyroid US reports: a comparative study with human readers.
Article in Endocrine, 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.
- Large language models for analyzing contrast-enhanced ultrasound reports of pancreatic cystic lesions.Quantitative imaging in medicine and surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of two large language models (LLMs) in extracting and assigning the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) categories from free-text thyroid ultrasound (US) reports.
methodsThis retrospective study included thyroid US reports from patients who underwent thyroid examinations. Three experienced US radiologists (with > 15 years of experience) and three general radiologists (with 4–5 years of experience) reviewed these free-text reports and assigned an ACR TI-RADS category based on the original US descriptions. The same reports were processed using standardized prompts in GPT-4.1 and Gemini-2.5-pro. Inter-rater agreement was evaluated using Gwet’s AC1 coefficient.
resultsInter-rater agreement between human readers was moderate (Gwet’s AC1 = 0.64, 95% CI 0.62–0.68) and was significantly higher than that between two LLMs (Gwet’s AC1 = 0.47, 95% CI 0.44–0.50; P < 0.001). Agreement between expert radiologists and the LLMs was substantial (Gwet’s AC1 = 0.67, 95% CI 0.65–0.70 for GPT-4.1 and 0.66, 95% CI 0.63–0.69 for Gemini-2.5-pro), whereas agreement with general readers was lower (Gwet’s AC1 = 0.49, 95% CI 0.46–0.52 for GPT-4.1, and 0.47, 95% CI 0.44–0.50 for Gemini-2.5-pro). The AUCs were 0.859 (95% CI, 0.831–0.887) for expert radiologists, 0.827 (95% CI, 0.798–0.856) for general radiologists, 0.836 (95% CI, 0.813–0.859) for Gemini-2.5-pro, and 0.811 (95% CI, 0.782–0.839) for GPT-4.1.
conclusionLLMs demonstrated promising performance in assigning TI-RADS category from free-text thyroid US reports, achieving accuracy and agreement comparable to that of general radiologists. However, they are not yet ready to replace expert interpretation in high-stakes clinical settings.
Indexed as
Identifiers
42067715What 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.