## 2 Related Works  
Image-to-text is a field of study within machine learning and computer vision, also known as automatic caption generation from input images. The central concept is to develop models capable of receiving an image, processing it, and generating a corresponding descriptive caption [21]. These models are trained on extensive datasets, such as Common Objects in Context (COCO), which includes over 200,000 annotated images, each accompanied by descriptive captions provided by humans [22]. Through these data, machine learning models learn the correlation between visual elements in the images and the words in the captions. Based on the capabilities of image-to-text, researchers have decided to explore the feasibility of applying this methodology to the interpretation of medical images, aiming to automatically generate medical reports to assist professionals in diagnosis [23]. These initiatives have prompted the development of several studies employing natural language processing (NLP), computer vision, and machine learning methods. These studies aim to create generative artificial intelligence systems capable of producing medical reports from medical images through computational processes. For example, Ref. [24] in 2017 used Bladder Cancer exams to generate medical reports with the Multimodal Mapping Approach. Ref. [25] in 2018 employed chest X-ray exams to produce medical reports using the CNN long short-term memory approach. Ref. [26] in 2023 utilized GI endoscopy to generate medical reports using the Multi-Modal Memory Transformer Network. Ref. [27] in 2019 used chest X-ray exams to generate medical reports with Multi-Attention and incorporating Background Information. Ref. [28] in 2023 employed chest X-ray exams to generate medical reports with the CNN Transformer approach. Ref. [29] in 2023 utilized chest X-ray exams to generate medical reports using the CNN Transformer approach. Ref. [30] in 2024 used chest X-ray exams to generate medical reports with CNNs Transformers. Ref. [31] in 2024 employed chest X-ray exams to generate medical reports with the CNN Transformer approach. Ref. [32] in 2024 used retinal images to generate medical reports with the CNN Transformer approach. Ref. [33] in 2024 employed Brain computed tomography exams to generate medical reports with the CNN GPT-2 approach. In this section, we introduce several relevant studies in the field of automatic medical report generation. Below, we present the strengths and weaknesses of these works. The strengths are as follows:  
1. Improved performance: The study [31] demonstrates superior performance in the validation metrics, showing the effectiveness of the proposed model in generating medical reports from radiographs. Other works, such as [32], also show promising results in terms of performance, highlighting the feasibility of their approaches;
2. Integration of Medical Knowledge: The work [28] integrates historical data and general information to enhance the generation of reports, resulting in more accurate and informative reports. This approach is complemented by the study [33];
3. Comprehensive experiments: Studies like [32; 33] conduct extensive experiments on various datasets, demonstrating the effectiveness of their approaches compared to advanced methods, which strengthens the validity of the results;
4. Use of pre-trained models: In the study [33], various pre-trained CNN models are explored to evaluate the best results. The weaknesses are as follows:  
1. Lack of dataset details: Some studies, such as [25; 26; 29], do not provide detailed information about the datasets used, which can compromise the generalization and robustness of the results. The study by [26] uses a relatively small dataset (3,069 exams accompanied by reports), while [25; 31] use datasets with 3643 and 3973 exams, respectively;
2. Generalization limitations: The work [28] may face challenges in generalizing to different pathologies or clinical contexts due to biases in the patient histories;
3.