## 5 Conclusions  
This work presented a triage-oriented CDSS architecture that integrates LLMs, FL, and structured AI pipelines in a privacy-conscious and modular design. The system is capable of processing multimodal healthcare inputs, providing actionable recommendations, and adapting to distributed institutional settings. We demonstrated the effectiveness of this system through two complementary evaluation settings. First, fine-tuned language models were benchmarked on the USMLE dataset, showing reproducible performance on general clinical-reasoning tasks. Second, a specialty-specific case study in infectious disease emergency triage demonstrated how the components of the system operate in concert to process real-world presentations and generate actionable triage outputs. The architecture correctly predicted triage levels, suggested appropriate infection-focused diagnoses, and provided supporting citations aligned with sepsis and antimicrobial guardelines. Our findings affirm that LLMs can be integrated with classical AI components to build adaptive CDSS platforms. The use of FL further enables data privacy while promoting institutional collaboration. All datasets, trained models, source code, and training scripts required to reproduce our experiments are provided in Supplementary Section S5. The proposed architecture serves as a foundational blueprint for next-generation AI systems in healthcare. It is designed to scale, adapt, and learn--supporting clinicians not only in emergency triage but across specialties and care pathways. The ID case study achieved 75.8% first-pass concordance with specialist review, underscoring the clinical reliability of the pipeline. As healthcare continues to evolve toward data-driven, interoperable, and personalized systems, the proposed CDSS offers a compelling pathway toward intelligent and ethical clinical decision-making. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15158412/s1](https://www.mdpi.com/article/10.3390/app15158412/s1)]([https://www.mdpi.com/article/10.3390/app15158412/s1](https://www.mdpi.com/article/10.3390/app15158412/s1)), Table S1: Four-pillar GDPR compliance framework mapping Regulation (EU) 2016/679 articles to technical and organisational safeguards in the CDSS; Supplementary Sections S1-S5: Detailed documentation covering tech stack rationale, workflow integration, privacy/security provisions, qualitative ablation insights, and data/code artifacts for reproduction. Conceptualization, A.K., O.T.D. and F.N.A. ; software, A.K. ; data curation and investigation, A.K., B.D. and O.T. ; writing--original draft, A.K., B.D. and F.N.A. ; writing--review and editing, O.T.D., B.D. and O.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study used the MIMIC-III v1.4 database, which is fully de-identified in compliance with the U.S. HIPAA. Access was obtained through the credentialed data-use process of PhysioNet after completion of the required training course and acceptance of the data-use agreement. Because the dataset is publicly available and de-identified, no additional Institutional Review Board (IRB) approval was required for this secondary analysis. Not applicable. The reproducibility artifacts supporting this study are provided in Supplementary Materials, which includes code, datasets, and evaluation outputs for both the USMIE-style benchmark and the infectious disease use case. Additional clinical data (e.g., MIMIC-III) is publicly available from PhysioNet at [[https://physionet.org/content/mimiciii/1.4](https://physionet.org/content/mimiciii/1.4)]([https://physionet.org/content/mimiciii/1.4](https://physionet.org/content/mimiciii/1.4)). (accessed on 25 June 2025) under appropriate credentialed access.