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

Journal: JMIR formative research
Published Date: Aug 19, 2025

Abstract

BACKGROUND: Thyroid nodules are common, with ultrasound imaging as the primary modality for their assessment. Risk stratification systems like the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) have been developed but suffer from interobserver variability and low specificity. Artificial intelligence, particularly large language models (LLMs) with multimodal capabilities, presents opportunities for efficient end-to-end diagnostic processes. However, their clinical utility remains uncertain.

Authors

  • Gerald Gui Ren Sng
    1Department of Endocrinology, Singapore General Hospital, Singapore.
  • Yi Xiang
    Department of Ophthalmology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Daniel Yan Zheng Lim
    3Health Services Research Unit, Singapore General Hospital, Singapore.
  • Joshua Yi Min Tung
    Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore, Singapore.
  • Jen Hong Tan
    Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
  • Chiaw Ling Chng
    Department of Endocrinology, Singapore General Hospital, 20 College Road, Academia Level 3, Singapore, 169856, Singapore, 65 63214377.

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