ArticleScientific reports2026
A comparative study of large language models (ChatGPT/DeepSeek) in generating structured thyroid ultrasound reports.
Article in Scientific reports, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The importance of structured radiology reports is well recognized for enabling efficient data extraction and facilitating multidisciplinary collaboration. This study aimed to evaluate the agreement rate and reproducibility of the large language models ChatGPT-4o and DeepSeek-R1 in generating structured thyroid ultrasound reports. This study retrospectively included 174 thyroid ultrasound reports from 174 patients, encompassing a total of 230 nodules. ChatGPT-4o and DeepSeek-R1 were used to convert the reports into structured formats according to the C-TIRADS guidelines. Two ultrasonographers assessed the agreement rate of the generated nodule classifications and the appropriateness of the management recommendations. Each report was submitted twice to evaluate the consistency of nodule classification and management suggestions. Among 174 patients (mean age 44 ± 11 years; 32 males), there was no significant difference in nodule classification agreement rate between ChatGPT-4o and DeepSeek-R1 (80.4% vs. 77.2%; OR = 1.636; 95% CI: 0.976-2.741; P = 0.205). ChatGPT-4o outperformed DeepSeek-R1 in providing more comprehensive or correct management recommendations (OR = 7.362, 95% CI: 4.255-12.735, P < 0.001). Furthermore, both ChatGPT-4o and DeepSeek-R1 demonstrated moderate consistency in nodule classification (AC1 = 0.767 vs. 0.713). Specifically, for category 3 nodules, both models showed high consistency (AC1 = 0.983 vs. 0.929). Compared to DeepSeek-R1, ChatGPT-4o exhibited higher consistency in providing management recommendations (AC1 = 0.809 vs. AC1 = 0.632). This study suggests that both ChatGPT and DeepSeek showed potential for converting free-text thyroid ultrasound reports into a structured format. While ChatGPT-4o and DeepSeek-R1 performed similarly in nodule classification agreement rate, ChatGPT-4o demonstrated a clear advantage in the agreement rate of management recommendations.
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.