Assessing ChatGPT's performance in national nuclear medicine specialty examination: An evaluative analysis

Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
View: 87

This Paper With 6 Page And PDF Format Ready To Download

  • Certificate
  • من نویسنده این مقاله هستم

استخراج به نرم افزارهای پژوهشی:

لینک ثابت به این Paper:

شناسه ملی سند علمی:

JR_IRJNM-32-1_010

تاریخ نمایه سازی: 8 بهمن 1402

Abstract:

Introduction: The rapid development of artificial intelligence (AI) has sparked a desire to analyse its potential applications in medicine. The aim of this article is to present the effectiveness of the ChatGPT advanced language model in the context of the pass rate of the polish National Specialty Examination (PES) in nuclear medicine. It also aims to identify its strengths and limitations through an in-depth analysis of the issues raised in the exam questions.Methods: The PES exam provided by the Centre for Medical Examinations in Łódź, consisting of ۱۲۰ questions, was used for the study. The questions were asked using the openai.com platform, through which free access to the GPT-۳.۵ model is available. All questions were classified according to Bloom's taxonomy to determine their complexity and difficulty, and according to two authors' subcategories. To assess the model's confidence in the validity of the answers, each questions was asked five times in independent sessions.Results: ChatGPT achieved ۵۶%, which means it did not pass the exam. The pass rate is ۶۰%. Of the ۱۱۷ questions asked, ۶۶ were answered correctly. In the percentage of each type and subtype of questions answered correctly, there were no statistically significant differences.Conclusion: Further testing is needed using the questions provided by Centre for Medical Examinations from the nuclear medicine specialty exam to evaluate the utility of the ChatGPT model. This opens the door for further research on upcoming improved versions of the ChatGPT.

Authors

Jakub Kufel

Department of Biophysics, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Michał Bielówka

Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Marcin Rojek

Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Adam Mitręga

Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Łukasz Czogalik

Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Dominika Kaczyńska

Professor Zbigniew Religa Student Scientific Association, Department of Biophysic, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

Dominika Kondoł

Wielospecjalistyczny Szpital Powiatowy S.A. im. dr B. Hagera Pyskowicka ۴۷-۵۱,۴۲-۶۱۲, Tarnowskie Góry, Poland

Kacper Palkij

Wielospecjalistyczny Szpital Powiatowy S.A. im. dr B. Hagera Pyskowicka ۴۷-۵۱,۴۲-۶۱۲, Tarnowskie Góry, Poland

Sylwia Mielcarska

Department of Medical and Molecular Biology, Faculty of Medical Sciences, Medical University of Silesia, Zabrze, Poland

مراجع و منابع این Paper:

لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :
  • The Lancet Digital Health. ChatGPT: friend or foe? Lancet Digit ...
  • Levin G, Horesh N, Brezinov Y, Meyer R. Performance of ...
  • Foreland M. Bloom’s Taxonomy. In: Orey M, editor. Emerging perspectives ...
  • Oztermeli AD, Oztermeli A. ChatGPT performance in the medical specialty ...
  • نمایش کامل مراجع