ArticleNPJ digital medicine2025
Multimodal GPT model for assisting thyroid nodule diagnosis and management.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 3 of them syntheses that pooled it.
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
39 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Bibliometric and Latent Dirichlet Allocation (LDA) analysis of artificial intelligence for thyroid nodules.Frontiers in endocrinology · 2026Pooled it
- Artificial intelligence in preoperative diagnosis of thyroid nodules: a bibliometric analysis.Frontiers in oncology · 2026Pooled it
- Diagnostic performance of ultrasound characteristics-based artificial intelligence models for thyroid nodules: a systematic review and meta-analysis.Frontiers in oncology · 2025Pooled it
- Genomics and Multi-Omics to Guide Clinical Management in Thyroid Cancer.International journal of molecular sciences · 2026Review
- Artificial intelligence in thyroid nodules and cancer: clinical validation and real-world performance.Endocrine connections · 2026Article
- Deep Learning-Based Computer-Aided Detection and Diagnosis System for Malignant Biliary Stricture (With Video).Cancers · 2026Article
- Machine learning approaches for predicting differentiated thyroid cancer recurrence using thyroglobulin levels and whole-body scans: a retrospective cohort study.Scientific reports · 2026Article
- Radiomics in thyroid nodule assessment.BMC medical imaging · 2026Review
- Precision medicine in thyroid nodules: current strategies, challenges, and future directions.Hormones (Athens, Greece) · 2026Review
- SCAR-Net-assisted ultrasound diagnosis of postoperative scars and recurrent lesions in breast cancer.iScience · 2026Article
- Evaluation of large language models for TI-RADS category extraction from free-text thyroid US reports: a comparative study with human readers.Endocrine · 2026Article
- Artificial intelligence-assisted quality assessment of mid-trimester ultrasound examinations using large vision-language models.BMC pregnancy and childbirth · 2026Article
- Prediction of malignancy and metastasis of thyroid cancer by combined feature sets through advanced machine learning.BMC medical informatics and decision making · 2026Article
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- Development and validation of a predictive nomogram for high-risk thyroid nodules: a retrospective analysis of sedentary time, insomnia, and elevated weight.Frontiers in oncology · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- A reliable and explainable deep learning framework for clinical-grade endocrine disorder risk prediction and decision support.Frontiers in artificial intelligence · 2026Article
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Precision oncology: from large language models to multi-agent systems.Frontiers in oncology · 2026Review
- Pathological diagnosis of thyroid nodules directly from ultrasonography by a weakly supervised deep learning framework.Frontiers in endocrinology · 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
29 authors.
Funding
Abstract
Although using artificial intelligence (AI) to analyze ultrasound images is a promising approach to assessing thyroid nodule risks, traditional AI models lack transparency and interpretability. We developed a multimodal generative pre-trained transformer for thyroid nodules (ThyGPT), aiming to provide a transparent and interpretable AI copilot model for thyroid nodule risk assessment and management. Ultrasound data from 59,406 patients across nine hospitals were retrospectively collected to train and test the model. After training, ThyGPT was found to assist in reducing biopsy rates by more than 40% without increasing missed diagnoses. In addition, it detects errors in ultrasound reports 1,610 times faster than humans. With the assistance of ThyGPT, the area under the curve for radiologists in assessing thyroid nodule risks improved from 0.805 to 0.908 (p < 0.001). As an AI-generated content-enhanced computer-aided diagnosis (AIGC-CAD) model, ThyGPT has the potential to revolutionize how radiologists use such tools.
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.