Accuracy of large language models in thyroid nodule-related questions based on the Korean thyroid imaging reporting and data system (K-TIRADS)
Künye
Kaba, E., Hürsoy, N., Solak, M., & Çeliker, F. B. (2024). Accuracy of Large Language Models in Thyroid Nodule-Related Questions Based on the Korean Thyroid Imaging Reporting and Data System (K-TIRADS). Korean journal of radiology, 25(5), 499–500. https://doi.org/10.3348/kjr.2024.0229Özet
We read with great pleasure the review article “Updated
Primer on Generative Artificial Intelligence and Large
Language Models in Medical Imaging for Medical
Professionals” by Kim et al. [1] which was published online
in the Korean Journal of Radiology in February. The authors
impressively presented a very comprehensive overview of
generative artificial intelligence, and also discussed the
background and working principles of large language models
(LLMs). Inspired by this article, we would like to present
this letter, in which we investigate the performance of LLMs
on questions related to thyroid nodules based on the Korean
Thyroid Imaging Reporting and Data System (K-TIRADS).