### Background  
Early CDSS relied on fixed if-then rules or simple acuity scores--transparent yet brittle when confronted with complex, heterogeneous data. Subsequent work introduced classical machine-learning models that used vitals and chief complaints to improve triage accuracy, but these approaches still struggled with free-text notes and could not share insights across institutions because of privacy barriers [15]. These limitations spurred the adoption of transformer-based LLMs for complex, mixed-format data and privacy-preserving methods--especially FL--to enable cross-institutional training without sharing raw patient data. Attention Mechanisms in Clinical Contexts.Attention Mechanisms in Neural Architectures have shown strong performance in focusing computational resources on the most relevant input features, thereby improving efficiency and accuracy. In clinical settings, these mechanisms have been extensively applied to medical image analysis tasks--such as segmentation, detection, and classification--helping systems highlight important features in modalities like CT, MRI, and X-ray imaging [16]. Similar attention-based mechanisms have been shown to improve real-time tracking performance in other domains, such as poultry monitoring, where the YOLO-Chicken algorithm enhanced detection accuracy and efficiency through attention modules [17]. This demonstrates the potential utility of lightweight yet precise attention-based architectures in clinical triage scenarios, where rapid and accurate interpretation of medical images or signals is essential. LLMs in Healthcare.The use of transformer-based LLMs in healthcare has rapidly expanded. Early efforts like ClinicalBERT and BioBERT, fine-tuned BERT variants on clinical notes, improving clinical concept extraction and note classification, Named Entity Recognition (NER) and relation extraction [18; 19]. LLMs have been applied to tasks such as summarizing patient histories, answering clinicians' questions, and even generating draft reports. Notably, GPT-3.5 and GPT-4 demonstrated the ability to answer medical exam questions at a level approaching or surpassing human medical graduates [20]. In a 2024 study, [PERSON] et al. evaluated a specialized GPT-4 (termed ChatGPT-4 Omni) on United States Medical Licensing Examination (USMLE) questions, finding it achieved 89-91% accuracy, significantly higher than the \(\sim\)60% average of medical students [11]. Privacy-Preserving AI and FL.Ensuring patient privacy while benefiting from large-scale data is a central theme in current research. FL has been widely studied as an approach to train AI models on distributed data. Several systematic reviews have cataloged the uses of FL in healthcare, noting applications in medical imaging, remote patient monitoring, and predictive modeling [21]. A 2024 survey focusing on smart-healthcare deployments catalogs 70+ FL use-cases across sensors, imaging, and EHR data [22]. The work of [PERSON] et al. demonstrated feasibility for deep models on imaging data [7], and others like [PERSON] et al. have extended FL to hospital wearable sensor networks, showing that vital sign anomaly detectors can be trained collaboratively without sharing raw data [23]. In a follow-up study, [PERSON] et al. validated the practicality of FL in a multi-institutional medical imaging context, showing comparable accuracy to centralized training [24]. Nevertheless, vanilla FL has limitations: communication overhead, data heterogeneity between sites, and the risk of privacy leakage from model updates [25]. Recent frameworks specifically aim to integrate FL with LLM training. For instance, [PERSON] et al. introduced FATE-LLM [26], an industrial-grade FL framework tailored for LLMs. In parallel, hybrid federated approaches combining meta-heuristic and classifier ensembles have been demonstrated for cardiovascular disease prediction [27].