Author Response: Comment: Is ChatGPT a Reliable Auxiliary Tool in Basic Life Support Training and Education? A Cross-sectional Study

We thank the authors for their thoughtful comments on our article, "Is ChatGPT a Reliable Auxiliary Tool in Basic Life Support (BLS) Training and Education?".Their observations highlight important methodological considerations and future directions for research in this evolving field.We agree that the use of open-ended prompts introduces variability.This choice was intentional, as it reflects the real-world clinical scenarios in which healthcare providers and learners must interpret and respond to unstructured information.In a similar article, Onder CE et al. reported that ChatGPT-4 showed moderate to good reliability in evaluating responses related to hypothyroidism in pregnancy when assessed through open-ended questions and real-world patient scenarios. 1Nonetheless, as suggested, future studies should indeed compare performance across different input formats, including multiple-choice and structured checklists.Regarding the strict scoring criteria, we acknowledge that partially correct answers may contain educational value.However, we adopted a "perfect-only" scoring system to ensure conservative accuracy estimates and to avoid overstating ChatGPT's reliability in a high-stakes context such as resuscitation education.Our study emphasized the use of auxiliary learning approaches in managing patients within real-world settings, where ensuring accuracy is essential to avoid endorsing fabricated responses.Similar conclusions were drawn by Shiferaw MW et al., who examined the accuracy and quality of artificial intelligence (AI) chatbot-generated responses in guiding patient-specific drug therapy and healthcare decisions. 2 We also agree with the need for gold-standard references such as ILCOR checklists.In our exploratory study, responses were evaluated by experienced BLS-certified faculty to simulate the judgment learners might receive in a classroom or bedside teaching environment.Incorporating structured guideline-based references in future work will certainly strengthen reproducibility.The concern about "prompt overfitting" is valid, and we minimized this by using new chat sessions for each iteration and prompting at different times of day.However, we recognize that repeated testing may still influence response patterns.Finally, we concur that hallucinations and fabricated yet plausible-sounding responses remain an inherent limitation of large language models.This underscores the importance of supervised and critical use of AI in medical education.We agree that integration with simulation platforms, evaluation of clinical outcomes, and exploration of centaur versus cyborg models of human-AI interaction will be valuable extensions of this work.We thank the authors again for their constructive input, which we believe will advance dialogue on the safe and effective integration of AI in resuscitation education.