## 5 Discussion
### Implications  
The implications of this study are particularly relevant for two key audiences: (i) medical students and international graduates preparing for licensing exams (particularly the Revalida), and (ii) healthcare professionals interested in exploring the use of LLMs in clinical decision support systems. For the former, LLMs may serve as valuable educational tools by reinforcing knowledge and offering structured justifications aligned with clinical reasoning. For the latter, although the current results highlight the potential of LLMs to process complex medical content in PT-BR, their application should remain strictly assistive, with outputs subject to professional review and validation. The findings demonstrate that some LLMs, particularly GPT-4o (79 out of 86 correct answers when using RAG; refer to Section 4.4), are capable of performing reasonably well on the objective questions of the Revalida exam. This level of performance suggests that these models have the potential as supportive tools in medical education, particularly for knowledge reinforcement [46; 47]. The qualitative analysis further revealed that GPT-4o's explanations were well-aligned with expert medical reasoning, offering structured and clinically coherent justifications. These results are consistent with prior studies with language models [48; 49], reinforcing the idea that LLMs could serve as valuable resources for healthcare. The ability of LLMs to perform well on licensing exam questions (particularly those that integrate clinical reasoning) suggests a foundational alignment with the types of decisions encountered in practice. While answering multiple-choice questions is not equivalent to practicing medicine, these tasks do reflect core knowledge and judgment processes that AI systems may eventually support in real-world clinical workflows. Despite this promising outlook, LLMs, especially when used in PT-BR language contexts, are not yet ready for autonomous integration into clinical workflows. The inconsistencies observed between test entries, the presence of fallback responses, and the risk of hallucinated content highlight ongoing limitations in reliability, accuracy, and contextual understanding. Moreover, medical knowledge and policies vary across countries, further complicating the applicability of LLMs in diverse clinical and educational settings [14]. In high-stakes domains like healthcare, even minor errors can have significant consequences, underscoring the need for careful validation and oversight. Therefore, LLMs should be regarded as complementary tools in clinical decision support, not as replacements for the expertise and judgment of healthcare professionals. While their performance in objective assessments is encouraging, human competence remains essential, particularly for tasks involving clinical judgment, ethical considerations, and patient-centered care. Any outputs generated by these models must be critically evaluated and validated by qualified professionals before being used in practice. With proper oversight, LLMs may help streamline access to information and support clinical reasoning, but their integration into healthcare must be approached cautiously and responsibly. The final phase of the study demonstrated that LLM performance can be further enhanced through domain adaptation strategies grounded in prior exam content. By leveraging justifications generated from previous Revalida editions, it was possible to improve model accuracy through both RAG and fine-tuning. This mirrors common practices among medical students and exam candidates, who frequently study past questions to reinforce learning and recognize patterns. The effectiveness of this strategy highlights a promising avenue for aligning LLM outputs with real-world reasoning expectations. As such, incorporating curated historical content may represent a practical and scalable approach to improving the relevance and trustworthiness of LLMs in medical education and assessment contexts.