## 8 Discussion
In clinical practice, these scores are highly relevant as they provide an objective measure of the accuracy and quality of automated descriptions compared to the observations made by experienced radiologists. A BLEU-4 score of 0.76, for example, suggests that the model is effective at replicating the language used in medical reports, which can speed up report generation and reduce the radiologists' workload. Similarly, the METEOR and ROUGE-L scores, which reached values of up to 0.50 and 0.81, respectively, indicate strong agreement in the structure and content of the reports, highlighting the model's ability to capture critical details from radiological images. However, the variability of scores in different clinical scenarios underscores the need for continuous improvements, particularly in detecting subtle anomalies. It is important to note, however, that these metrics do not always represent a realistic analysis of the model's capability. Variations in word choice and phrasing can result in relatively low scores even when the model generates reports as precise as those of the physicians. For example, the use of synonyms or different sentence structures can lower the scores despite equivalent clinical content. This indicates that, while the BLEU, METEOR, and ROUGE-L metrics are useful for an initial assessment, a deeper and qualitative analysis is necessary to fully evaluate the model's effectiveness. Therefore, the full integration of this technology into clinical practice should consider both quantitative and qualitative analyses to ensure the accuracy and utility of the generated reports. This discussion suggests that, while the model shows promising performance in describing normal clinical features and identifying specific findings, further adjustments are needed to improve the detection of early anomalies and mitigate the impact of linguistic variation. With these enhancements, such as adding new exam samples to the dataset, the model has great potential to be a valuable tool in clinical practice, aiding in the accurate and efficient interpretation of Polydactyly X-rays. The proposed methodology has shown promising results in the automatic generation of Polydactyly radiograph reports, indicating its potential for near-term implementation in a real clinical setting. The model's robust performance on metrics such as BLEU, METEOR, and ROUGE-L, especially in normal cases, suggests that it can be a valuable tool to assist specialists in drafting medical reports, thus alleviating the workload of radiologists and increasing diagnostic efficiency. However, some limitations must be addressed before practical deployment. The performance gap between normal and abnormal cases highlights the need to enhance the model's accuracy in describing abnormal findings, which can be achieved by expanding the dataset and refining learning techniques. Therefore, despite encouraging results, additional efforts are needed to refine the methodology and ensure its effectiveness and safety in a real clinical environment. It is important to emphasize that the proposed system should be used as a second opinion or pre-diagnosis tool, with the final confirmation and review of medical reports being the sole responsibility of physicians. This approach ensures that the model assists specialists without replacing human clinical evaluation. This discussion suggests that, while the model shows promising performance in describing normal clinical features and identifying specific findings, further adjustments are needed to improve the detection of early anomalies and mitigate the impact of linguistic variation. With these enhancements, such as adding new exam samples to the dataset, the model has great potential to be a valuable tool in clinical practice, aiding in the interpretation of Polydactyly X-rays with accuracy and efficiency.