Building a Structured Reasoning AI Model for Legal Judgment in Telehealth Systems

Abstract: This paper introduces a structured AI framework developed to support legal decision-making in telehealth environments. Most existing systems either rely on user declarations or conduct audits after the fact. Our model takes a different approach: it integrates legal reasoning into the AI’s core logic. The system is built on an architecture that includes structured semantic modeling, executable legal logic, and role-specific response generation. Beyond enabling real-time legality checks, the model also accounts for ambiguity in legal language by incorporating fuzzy clauses, confidence scores, and guidance pathways that reflect varying levels of legal certainty. Instead of treating compliance as a checklist imposed from outside, the model treats it as something that unfolds from within the system's logic. The result is an AI that can explain its decisions, adapt to legal environments, and support institutional accountability. This work offers a new perspective on how AI can operate not just as a tool, but as a responsible actor in regulated clinical systems, and points to new directions for designing legally responsive telehealth platforms. Keywords: artificial intelligence, explainable AI, legal compliance, telehealth systems, semantic reasoning, health law