Agentic-AI Healthcare: Multilingual, Privacy-First Framework with MCP Agents
Mohammed A. Shehab
TL;DR
The paper tackles the challenge of building trustworthy healthcare AI that is privacy-preserving, multilingual, and explainable. It proposes a modular agentic platform coordinated by the Model Context Protocol (MCP), with a dedicated Privacy & Compliance Layer implementing RBAC and AES-GCM field-level encryption. The contributions include a working prototype that demonstrates multilingual interactions (English, French, Arabic), explainable LLM-driven reasoning, and a compliance-first architecture, all orchestrated within a layered, secure stack. This work shows the feasibility of combining agent orchestration with privacy-by-design in healthcare AI, providing a blueprint for industry adoption by startups and cloud providers.
Abstract
This paper introduces Agentic-AI Healthcare, a privacy-aware, multilingual, and explainable research prototype developed as a single-investigator project. The system leverages the emerging Model Context Protocol (MCP) to orchestrate multiple intelligent agents for patient interaction, including symptom checking, medication suggestions, and appointment scheduling. The platform integrates a dedicated Privacy and Compliance Layer that applies role-based access control (RBAC), AES-GCM field-level encryption, and tamper-evident audit logging, aligning with major healthcare data protection standards such as HIPAA (US), PIPEDA (Canada), and PHIPA (Ontario). Example use cases demonstrate multilingual patient-doctor interaction (English, French, Arabic) and transparent diagnostic reasoning powered by large language models. As an applied AI contribution, this work highlights the feasibility of combining agentic orchestration, multilingual accessibility, and compliance-aware architecture in healthcare applications. This platform is presented as a research prototype and is not a certified medical device.