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Traditional public health surveillance often operates within fragmented data silos, leading to delayed outbreak recognition and suboptimal clinical responses. This conceptual systems research article presents the multi-source public health surveillance intelligence mesh (MPSIM). This original architectural paradigm interconnects heterogeneous data ecosystems into a resilient, outbreak-aware intelligence fabric. MPSIM synthesizes multi-modal inputs from electronic health records, genomic repositories, environmental sensors, and social-determinant streams through a theoretically defined mesh topology that supports continuous intelligence propagation and adaptive governance.The framework introduces a five-layer stratified architecture with a unique polyadic feedback topology enabling bidirectional drift correction and resource orchestration. Three interpretive conceptual formulas are advanced to model risk propagation, decision confidence, and governance load, furnishing system designers with abstract yet operationalizable constructs.MPSIM is positioned as a blueprint for next-generation, outbreak-aware healthcare systems that embed surveillance intelligence directly into clinical workflows while satisfying stringent governance and interoperability requirements. The architecture prioritizes theoretical scalability, ethical oversight, and seamless multi-source fusion to advance proactive containment strategies across diverse deployment environments.

This article proposes a conceptual framework for a diagnostic support system in emergency departments that leverages large language models, retrieval-augmented generation, and chain-of-thought reasoning. By combining triage notes and vital signs, the system generates a ranked differential diagnosis list to assist clinicians without replacing their judgment. The framework includes components like a triage note encoder, a vital sign encoder, a retrieval module, and a diagnosis ranker, using evidence from clinical guidelines, curated references, and de-identified prior cases. The approach grounds the model in authoritative knowledge while ensuring transparency and explainability in the diagnostic process. However, prospective validation, integration into workflows, and clinician oversight are crucial before implementation to ensure safety and effectiveness.

Anticoagulation management requires balancing multiple factors such as bleeding risk, thromboembolic risk, drug interactions, and renal function. Deep learning can assist in risk prediction, but its effectiveness relies on clinicians' ability to understand and verify the recommendations. Black-box models may recommend actions without providing clear explanations. In contrast, clinical guidelines are rule-based but not directly executable by neural models. This article introduces a neuro-symbolic XAI framework that combines deep learning predictions with explicit clinical guidelines. It includes a neural prediction module, a symbolic reasoning engine, and an integration layer for traceable justifications. The neuro-symbolic approach connects data-driven predictions to clinical rules, improving auditability and trustworthiness in decision support. This framework aims to enhance anticoagulation management by providing verifiable, clinician-understandable decision support, focusing on explainability-by-design.