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This paper is in the following e-collection/theme issue:

Development and Evaluation of Research Methods, Instruments and Tools (1369) Clinical Informatics (2337) Natural Language Processing (1312) Imaging Informatics (480) Machine Learning (3272) Artificial Intelligence (4894) Innovations in Cancer Diagnostic and Decision Support (157) Cancer Prognosis Models and Machine Learning (142) Generative Language Models Including ChatGPT (1577)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70863, first published .
Doctor examines young woman's throat during medical checkup.

A Multimodal Large Language Model as an End-to-End Classifier of Thyroid Nodule Malignancy Risk: Usability Study

A Multimodal Large Language Model as an End-to-End Classifier of Thyroid Nodule Malignancy Risk: Usability Study

Authors of this article:

Gerald Gui Ren Sng1, 2 Author Orcid Image ;   Yi Xiang3 Author Orcid Image ;   Daniel Yan Zheng Lim2, 4 Author Orcid Image ;   Joshua Yi Min Tung2, 5 Author Orcid Image ;   Jen Hong Tan2 Author Orcid Image ;   Chiaw Ling Chng1 Author Orcid Image
Article Authors Cited by (5) Tweetations Metrics

Journals

  1. Silva T, Andrade-Bortoletto M, Alencar-Palha C, Ocampo T, Oliveira-Santos C, Freitas D, Oliveira M. Evaluation of ChatGPT–4 and Microsoft Copilot for Third-Molar Assessment on Panoramic Radiographs. Journal of Imaging Informatics in Medicine 2026 View
  2. Zhang D, Li B, Ju H, Li T, Zhang Y. Hybrid deep feature and machine learning framework for classification of thyroid nodules in ultrasound images. Frontiers in Oncology 2026;16 View
  3. Jayaraman S, Sivasakthivel R, Jayapal J, Chinnaiyan B. An Explainable Multimodal Deep Learning Framework for Thyroid Nodule Diagnosis in Ultrasound Imaging Using Hybrid Vision Transformers and Med-PaLM. Computation 2026;14(6):138 View
  4. Cai D, Cai S, Ye J, Xu Z, Li W. A retrospective comparative study of ChatGPT-5.4 and DeepSeek-VL2 for C-TIRADS-based risk stratification of thyroid nodules on ultrasound images. Frontiers in Digital Health 2026;8 View
  5. Dong C, Liu Y, Yin J, Yan W, Sun J, Zhang X, Huo S, Yu F, Zhou Y. Artificial intelligence-assisted disease diagnosis: A narrative review of diagnostic advances and challenges toward clinical integration. DIGITAL HEALTH 2026;12 View