## 4 Discussion  
LLMs like ChatGPT by OpenAI and Bard by Google have the potential to be integrated into healthcare systems to assist with a wide range of tasks, from clinical decision-making to administrative functions. This study is the first to evaluate ChatGPT-4 and Google Bard in the context of occupational medicine. It focuses on their ability to generate open-ended responses to complex questions related to workplace safety and occupational risks. Our findings indicate that ChatGPT-4, when combined with legislative context, generally provides more accurate and complete answers than Bard. In Appendix D, we have included an example of a correct and complete response generated by [PERSON] and one by ChatGPT-4 (Table A2), as well as an example of an incorrect response, both in terms of completeness and accuracy, generated by both LLMs (Table A3). The literature highlights the focus on the evaluation of LLMs in answering multiple-choice questions in medical contexts. ChatGPT-4 demonstrated promising accuracy in bariatric surgery-related questions compared to Microsoft's Bing and Google Bard [23]. Similarly, ChatGPT-3.5 and 4 outperformed [PERSON] in radiology board practice questions [24]. ChatGPT-4 excelled in exams like the National Council Licensure Examination for Registered Nurses, the National Nursing Licensure Examination in China [25], clinical chemistry questions [26], and the Japanese national dental hygienist examination [27]. It was the only LLM to outperform others in the 2022 Family Medicine In-Training Exam [28]. [PERSON] was surpassed by both ChatGPT-3.5 and 4 in the Advanced Burn Life Support exam [29]. Additionally, ChatGPT-4 showed superior performance in the American Board of Physical Medicine and Rehabilitation exam [30], lung cancer-related questions [31], oral neurophysiology committee exams [32], and the Royal College of Ophthalmologists' exams in the United Kingdom [33]. When evaluating subject-specific responses, Bard outperforms ChatGPT-4 only in manual handling of loads and physical hazards. In chemical hazards, Bard exceeds ChatGPT-4+context solely in terms of completeness. This result can be explained by several factors, which are primarily related to the nature of the questions and the distinct characteristics of Bard and ChatGPT-4. Questions about manual handling were, in many cases, formulated with a more practical approach, such as how to lift a load safely. In these instances, Bard appears to perform better due to its access to updated internet resources, which may provide more recent or specific practical guidelines. Similarly, in the domain of physical and chemical hazards, Bard's superior performance may stem from its ability to retrieve threshold values or technical details, which are again more practical than regulatory questions. This reliance on dynamic and up-to-date information could give Bard an advantage in areas requiring precision and alignment with current standards. Additionally, Bard's performance differences may reflect inherent biases in the training data or optimization strategies. It is plausible that [PERSON]'s pretraining placed greater emphasis on technical resources, specific regulations, and manuals related to physical safety and manual handling. This focus might better align Bard with regional or international standards, such as European or U.S. regulations, which influence workplace safety guidelines and practices. Finally, these explanations remain speculative and are based on observed performance differences and the general characteristics of large language models (LLMs). Given that the exact details of the training processes and datasets used for Bard and ChatGPT-4 are not publicly available, further studies would be necessary to definitively determine the causes of the differences observed in this evaluation. Effect size calculations provided additional insights into the magnitude of the observed differences.