en Medical Education Medical Education Original Research Original Research <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-style:italic"><b><span style="font-size:9.0pt"><span style="font-style:normal">Background: </span></span></b><span style="font-size:9.0pt"><span style="font-style:normal">A 175 billion parameter transformer architecture is used by OpenAI&#39;s ChatGPT language model to perform tasks requiring natural language processing. This study aims to evaluate the knowledge and interpretive abilities of ChatGPT on three types of Iranian medical license exams: basic sciences, pre-internship, and pre-residency.</span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Methods: </b>This comparative study involved administering three different levels of Iran&#39;s medical license exams, which included basic sciences, pre-internship, and pre-residency, to ChatGPT 3.5. Two versions of each exam were used, corresponding to the ChatGPT 3.5&#39;s internet access time: one during the access time and one after. These exams were inputted to ChatGPT in Persian and English. The accuracy and concordance of each question were extracted by two blinded adjudicators. </span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Results:</b> A total of 2210 questions, including 667 basic sciences, 763 pre-internship, and 780 pre-residency questions, were presented to ChatGPT in both English and Persian languages. Across all tests, the overall accuracy was found to be 48.5%, with an overall concordance of 91%. Notably, English questions exhibited higher accuracy and concordance rates, with 61.4% accuracy and 94.5% concordance, compared to 35.7% accuracy and 88.7% concordance for Persian questions.</span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span style="font-family:&quot;Times New Roman&quot;,serif"><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Conclusion:</b>&nbsp; Our findings demonstrate that ChatGPT performs above the required passing scores on basic sciences and pre-internship exams. Moreover, ChatGPT could obtain the minimal score needed to apply for residency positions in Iran; however, it was lower than the applicants&#39; mean scores. Significantly, the model showcases its ability to provide reasoning and contextual information in the majority of responses. These results provide compelling evidence for the potential use of ChatGPT in medical education.</span></span></span></span></span></span></span></span><br> &nbsp; Medical education, Chat GPT, Artificial intelligence, Iran 190 195 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-8953-1&slc_lang=en&sid=1 Alireza Keshtkar alireza.keshtkar.medstu@gmail.com 200319475328460088701 200319475328460088701 No Research Center of Noncommunicable Diseases, Jahrom University of Medical Sciences, Jahrom, Iran Ali-Asghar Hayat alihayat63@gmail.com 200319475328460088702 200319475328460088702 No Clinical Education Research Center, Department of Medical Education, School of Medicine. Shiraz University of Medical Sciences. Shiraz, Iran Farnaz Atighi atighifarn@sums.ac.ir 200319475328460088703 0009-0005-2353-7139 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Nazanin Ayare ayarenn@sums.ac.ir 200319475328460088704 200319475328460088704 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Mohammadreza Keshtkar keshtkarmh@sums.ac.ir 200319475328460088705 0009-0007-1642-0885 No Student Research Committee, Yazd University of Medical Sciences, Yazd, Iran Parsa Yazdanpanahi yazdanpanahipar@sums.ac.ir 200319475328460088706 0009-0004-3969-2830 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Erfan Sadeghi erfansadesghiprof@sums.ac.ir 200319475328460088707 200319475328460088707 No Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran Noushin Deilami dilami@sums.ac.ir 200319475328460088708 0009-0008-3756-3495 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Hamid Reihani hmid.reihani@gmail.com 200319475328460088709 200319475328460088709 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Alireza Karimi akarimialirezak@sums.ac.ir 200319475328460088710 0009-0008-5154-5074 No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Hamidreza Mokhtari mokhtarihamid@sums.ac.ir 200319475328460088711 0009-0006-2518-790X No Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran Mohammad Hashem Hashempur hashempur@gmail.com 200319475328460088712 200319475328460088712 Yes Research Center for Traditional Medicine and History of Medicine, Department of Persian Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran