Comparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. The proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. We conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain \glspl{mr}, achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. This paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.

翻译：在脑肿瘤评估中，比较诊断使得医疗中心能够利用现有信息，在新患者接受评估时比对相似病例。通过利用人工智能模型，所提出的系统能够为给定查询检索最相似的脑肿瘤病例。其主要目标是通过生成更精确的医学图像表示来增强诊断过程，尤其关注患者特定的正常特征与病理特征。该模型运用人工智能检测患者特征，以从数据库中推荐最相似的病例。系统不仅建议相似病例，还在其设计中平衡了健康与异常特征的表示。这不仅促进了其使用的普适性，也辅助临床医生进行决策。我们针对类似研究对本方法进行了比较分析。所提出的架构在患者的肿瘤区域和健康区域均获得了0.474的Dice系数，优于已有文献。我们提出的模型擅长从脑部\glspl{mr}中提取并融合解剖学与病理学特征，在依赖成本较低的标签信息的同时取得了最先进的结果，这显著降低了训练过程的总体成本。本文为进一步探索所提架构的广泛适用性与优化以增强临床决策提供了充分依据。本工作提出的新方法标志着医学诊断领域，特别是在人工智能辅助图像检索背景下取得了重要进展，并有望通过将人工智能作为支持工具而非黑箱系统来降低成本并提升患者护理质量。