# Agentic-AI Healthcare: Multilingual, Privacy-First Framework with MCP Agents

arXiv: https://arxiv.org/abs/2510.02325 · HTML: https://mlsanity.com/paper/2510.02325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 6 of 6)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [A semi-autonomous approach to connecting proprietary EHR standards to FHIR](https://mlsanity.com/paper/1911.12254/) (2019)
- [Implications of Artificial Intelligence on Health Data Privacy and Confidentiality](https://mlsanity.com/paper/2501.01639/) (2025)
- [Serverless on FHIR: Deploying machine learning models for healthcare on the cloud](https://mlsanity.com/paper/2006.04748/) (2020)
- [A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare](https://mlsanity.com/paper/2506.13904/) (2025)
- [Logging Requirement for Continuous Auditing of Responsible Machine Learning-based Applications](https://mlsanity.com/paper/2508.17851/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)

## Abstract (arXiv)

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.