## 4 Discussion
Regarding completeness, the effect size ([PERSON]'s d = 0.16; r = 0.08) suggests that,although the difference between ChatGPT-4+context and Bard is statistically significant, the effect is small and thus of limited practical significance. In other words, the completeness of the responses shows only a modest difference between the two models, despite the significant \(p\)-value. As for accuracy, the effect size ([PERSON]'s d = 0.40; r = 0.20) indicates a moderate difference between the two models, with ChatGPT-4+context performing significantly better than Bard. This suggests that ChatGPT-4+context's responses are more accurate to a meaningful degree, making the difference between the two models relevant in practical contexts, particularly in medical fields where accuracy is crucial. From the error analysis, it emerged that Bard had a higher error rate than ChatGPT-4 for all categories except for \"non-response\". ChatGPT's programming may lead it to decline answering certain questions such as those asking for personalized medical advice or involving sensitive information, preferring instead to recommend consulting a medical professional. In Appendix E, six tables are presented, each displaying one response from both LLMs, containing one of the six evaluated error categories. Specifically, Table 14 demonstrates cases of no response, Table 15 showcases incorrect definition, Table 16 includes instances of content error, Table 17 illustrates two examples of responses with a context error, Table 18 highlights responses with internal contradiction, and Table 19 presents examples of typo errors. Broadly speaking, in agreement with the aforementioned studies, Bard has shown a lower performance, and our results indicate that this is due to incorrect content in the text produced, even when simply asked for a definition. The presence of such a high rate of content errors (almost 30%) for Bard represents a significant limitation, as we deduce that its performance, despite its full and direct access to the internet, is currently unreliable and has a large margin for improvement. Additionally, Bard often makes use of incorrect contextual references. It is not possible to hypothesize the reason for such incorrect context retrieval, as it is unknown how Google selects the requested data. This type of error is more than halved in ChatGPT-4+context, thanks in part to the optimization of its semantic search process. Despite their promise, conversational AI systems present potential ethical risks, such as misinformation, privacy violations, reinforcement of the biases in training data, and the generation of plagiarized or inaccurate content. These risks are particularly concerning in healthcare, where accuracy is critical [34]. LLMs are prone to generating \"hallucinations,\" producing responses that are plausible but factually incorrect. Notably, studies have shown that 45% of ChatGPT's responses contain inaccuracies, and [PERSON]'s error rate is even higher, particularly in academic contexts [34]. Research and development are underway to improve the accuracy and reliability of conversational AI in medicine. For this reason, the World Health Organization (WHO) has recently released a comprehensive set of guidelines titled \"Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models\" [35], which is aimed at ensuring the ethical use of these generative AI technologies in healthcare. The WHO's guidance includes over 40 recommendations for governments, technology developers, and healthcare providers to manage the use of LMMs responsibly. The guidelines identify five areas of application for LMMs in healthcare: clinical diagnosis, patient-guided care, administrative tasks, medical training, and scientific research for drug development. However, the WHO also emphasizes the potential risks associated with LMMs. These include concerns about accuracy, potential biases, misinformation, and the ethical implications of using AI in sensitive healthcare settings.