Abstract
INTRODUCTION: Artificial intelligence (AI) based large language models (LLM) contain enormous potential in education and training. Recent publications demonstrated that they are able to outperform participants in written medical exams. RESEARCH QUESTION: We aimed to explore the accuracy of AI in the written part of the EANS board exam. MATERIAL AND METHODS: Eighty-six representative single best answer (SBA) questions, included at least ten times in prior EANS board exams, were selected by the current EANS board exam committee. The questions' content was classified as 75 text-based (TB) and 11 image-based (IB) and their structure as 50 interpretation-weighted, 30 theory-based and 6 true-or-false. Questions were tested with Chat GPT 3.5, Bing and Bard. The AI and participant results were statistically analyzed through ANOVA tests with Stata SE 15 (StataCorp, College Station, TX). P-values of <0.05 were considered as statistically significant. RESULTS: The Bard LLM achieved the highest accuracy with 62% correct questions overall and 69% excluding IB, outperforming human exam participants 59% (p = 0.67) and 59% (p = 0.42), respectively. All LLMs scored highest in theory-based questions, excluding IB questions (Chat-GPT: 79%; Bing: 83%; Bard: 86%) and significantly better than the human exam participants (60%; p = 0.03). AI could not answer any IB question correctly. DISCUSSION AND CONCLUSION: AI passed the written EANS board exam based on representative SBA questions and achieved results close to or even better than the human exam participants. Our results raise several ethical and practical implications, which may impact the current concept for the written EANS board exam.