GPT-4 passes most of the 297 written Polish Board Certification Examinations
<|reference_start|>GPT-4 passes most of the 297 written Polish Board Certification Examinations: Introduction: Recently, the effectiveness of Large Language Models (LLMs) has increased rapidly, allowing them to be used in a great number of applications. However, the risks posed by the generation of false information through LLMs significantly limit their applications in sensitive areas such as healthcare, highlighting the necessity for rigorous validations to determine their utility and reliability. To date, no study has extensively compared the performance of LLMs on Polish medical examinations across a broad spectrum of specialties on a very large dataset. Objectives: This study evaluated the performance of three Generative Pretrained Transformer (GPT) models on the Polish Board Certification Exam (Pa\'nstwowy Egzamin Specjalizacyjny, PES) dataset, which consists of 297 tests. Methods: We developed a software program to download and process PES exams and tested the performance of GPT models using OpenAI Application Programming Interface. Results: Our findings reveal that GPT-3.5 did not pass any of the analyzed exams. In contrast, the GPT-4 models demonstrated the capability to pass the majority of the exams evaluated, with the most recent model, gpt-4-0125, successfully passing 222 (75%) of them. The performance of the GPT models varied significantly, displaying excellence in exams related to certain specialties while completely failing others. Conclusions: The significant progress and impressive performance of LLM models hold great promise for the increased application of AI in the field of medicine in Poland. For instance, this advancement could lead to the development of AI-based medical assistants for healthcare professionals, enhancing the efficiency and accuracy of medical services.<|reference_end|>
@article{pokrywka2024gpt-4,
    title={GPT-4 passes most of the 297 written Polish Board Certification
  Examinations},
    author={Jakub Pokrywka and Jeremi Kaczmarek and Edward Gorzela'nczyk},
    journal={arXiv preprint arXiv:2405.01589},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.01589},
    primaryClass={cs.CL cs.AI}
}