ArticleEndocrine2025
Clinical application potential of large language model: a study based on thyroid nodules.
Article in Endocrine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- The Performance of ChatGPT-4o and DeepSeek-R1 in Interpreting Thyroid Nodule Ultrasound Text Reports: Multicenter Study.Journal of medical Internet research · 2026Article
- Evaluating Chain-of-Thought reasoning in large language models for thyroid ultrasound interpretation: a dual-information approach.Frontiers in artificial intelligence · 2026Article
- Appropriateness of Thyroid Nodule Cancer Risk Assessment and Management Recommendations Provided by Large Language Models.Journal of imaging informatics in medicine · 2025Article
- Variability of ChatGPT in Interpreting the Lexicon of ACR-TIRADS, EU-TIRADS, and K-TIRADS.Diagnostics (Basel, Switzerland) · 2025Article
- Intra- and Inter-Observer Reliability of ChatGPT-4o in Thyroid Nodule Ultrasound Feature Analysis Based on ACR TI-RADS: An Image-Based Study.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of Natural Language Processing in Otolaryngology: A Scoping Review.The Laryngoscope · 2025Article
- Comparing ChatGPT 4.0's Performance in Interpreting Thyroid Nodule Ultrasound Reports Using ACR-TI-RADS 2017: Analysis Across Different Levels of Ultrasound User Experience.Diagnostics (Basel, Switzerland) · 2025Article
- LLM evaluation for thyroid nodule assessment: comparing ACR-TIRADS, C-TIRADS, and clinician-AI trust gap.Frontiers in endocrinology · 2025Article
- Review
- Leveraging ChatGPT to assist junior physicians in standardizing ultrasound report writing: A promising tool.Digital healthArticle
Corrections and comments
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Authors and funding
20 authors.
Funding
Abstract
backgroundLimited data indicated the performance of large language model (LLM) taking on the role of doctors. We aimed to investigate the potential for ChatGPT-3.5 and New Bing Chat acting as doctors using thyroid nodules as an example.
methodsA total of 145 patients with thyroid nodules were included for generating questions. Each question was entered into chatbot of ChatGPT-3.5 and New Bing Chat five times and five responses were acquired respectively. These responses were compared with answers given by five junior doctors. Responses from five senior doctors were regarded as gold standard. Accuracy and reproducibility of responses from ChatGPT-3.5 and New Bing Chat were evaluated.
resultsThe accuracy of ChatGPT-3.5 and New Bing Chat in answering Q2, Q3, Q5 were lower than that of junior doctors (all P < 0.05), while both LLMs were comparable to junior doctors when answering Q4 and Q6. In terms of "high reproducibility and accuracy", ChatGPT-3.5 outperformed New Bing Chat in Q1 and Q5 (P < 0.001 and P = 0.008, respectively), but showed no significant difference in Q2, Q3, Q4, and Q6 (P > 0.05 for all). New Bing Chat generated higher accuracy than ChatGPT-3.5 (72.41% vs 58.62%) (P = 0.003) in decision making of thyroid nodules, and both were less accurate than junior doctors (89.66%, P < 0.001 for both).
conclusionsThe exploration of ChatGPT-3.5 and New Bing Chat in the diagnosis and management of thyroid nodules illustrates that LLMs currently demonstrate the potential for medical applications, but do not yet reach the clinical decision-making capacity of doctors.
Indexed as
Identifiers
39080210What 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.