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Comparative Evaluation of the Accuracies of Large Language Models in Answering VI-RADS-Related Questions

  • Eren Camur (Department of Radiology, Ministry of Health Ankara 29 Mayis State Hospital) ;
  • Turay Cesur (Department of Radiology, Ankara Mamak State Hospital) ;
  • Yasin Celal Gunes (Department of Radiology, TC Saglik Bakanligi Kirikkale Yuksek Ihtisas Hastanesi)
  • 투고 : 2024.05.10
  • 심사 : 2024.05.15
  • 발행 : 2024.08.01

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참고문헌

  1. Kaba E, Hursoy N, Solak M, Celiker FB. Accuracy of large language models in thyroid nodule-related questions based on the Korean thyroid imaging reporting and data system (K-TIRADS). Korean J Radiol 2024;25:499-500
  2. Akinci D'Antonoli T, Stanzione A, Bluethgen C, Vernuccio F, Ugga L, Klontzas ME, et al. Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions. Diagn Interv Radiol 2024;30:80-90
  3. Gunes YC, Cesur T. A comparative study: diagnostic performance of ChatGPT 3.5, Google Bard, Microsoft Bing, and radiologists in thoracic radiology cases. medRxiv [Preprint]. 2024 [accessed on January 20, 2024]. Available at: https://doi.org/10.1101/2024.01.18.24301495
  4. 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
  5. Panebianco V, Narumi Y, Altun E, Bochner BH, Efstathiou JA, Hafeez S, et al. Multiparametric magnetic resonance imaging for bladder cancer: development of VI-RADS (vesical imaging-reporting and data system). Eur Urol 2018;74:294-306