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Accuracy of Large Language Models in Thyroid NoduleRelated Questions Based on the Korean Thyroid Imaging Reporting and Data System (K-TIRADS)

  • Esat Kaba (Department of Radiology, Recep Tayyip Erdogan University) ;
  • Nur Hürsoy (Department of Radiology, Recep Tayyip Erdogan University) ;
  • Merve Solak (Department of Radiology, Recep Tayyip Erdogan University) ;
  • Fatma Beyazal Celiker (Department of Radiology, Recep Tayyip Erdogan University)
  • 투고 : 2024.03.06
  • 심사 : 2024.03.15
  • 발행 : 2024.05.01

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참고문헌

  1. Kim K, Cho K, Jang R, Kyung S, Lee S, Ham S, et al. Updated primer on generative artificial intelligence and large language models in medical imaging for medical professionals. Korean J Radiol 2024;25:224-242 
  2. Ha EJ, Chung SR, Na DG, Ahn HS, Chung J, Lee JY, et al. 2021 Korean thyroid imaging reporting and data system and imaging-based management of thyroid nodules: Korean Society of Thyroid Radiology consensus statement and recommendations. Korean J Radiol 2021;22:2094-2123 
  3. Elkassem AA, Smith AD. Potential use cases for ChatGPT in radiology reporting. AJR Am J Roentgenol 2023;221:373-376 
  4. Kim S, Lee CK, Kim SS. Large language models: a guide for radiologists. Korean J Radiol 2024;25:126-133 
  5. Haver HL, Gupta AK, Ambinder EB, Bahl M, Oluyemi ET, Jeudy J, et al. Evaluating the use of ChatGPT to accurately simplify patient-centered information about breast cancer prevention and screening. Radiol Imaging Cancer 2024;6:e230086 
  6. Gordon EB, Towbin AJ, Wingrove P, Shafique U, Haas B, Kitts AB, et al. Enhancing patient communication with chat-GPT in radiology: evaluating the efficacy and readability of answers to common imaging-related questions. J Am Coll Radiol 2024;21:353-359