# Agentic AI-driven Immersive Simulation: A Knowledge-Aware Virtual Training Platform forHigh Dose Rate (HDR) Brachytherapy

arXiv: https://arxiv.org/abs/2608.08163 · HTML: https://mlsanity.com/paper/2608.08163/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 21 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## 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, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [The Faiss library](https://mlsanity.com/paper/2401.08281/) (2024)
- [All One Needs to Know about Metaverse: A Complete Survey on Technological Singularity, Virtual Ecosystem, and Research Agenda](https://mlsanity.com/paper/2110.05352/) (2021)
- [A Full Dive into Realizing the Edge-enabled Metaverse: Visions, Enabling Technologies,and Challenges](https://mlsanity.com/paper/2203.05471/) (2022)
- [MEDCO: Medical Education Copilots Based on A Multi-Agent Framework](https://mlsanity.com/paper/2408.12496/) (2024)
- [LLMs Can Simulate Standardized Patients via Agent Coevolution](https://mlsanity.com/paper/2412.11716/) (2024)
- [Whisper: Fast Flooding for Low-Power Wireless Networks](https://mlsanity.com/paper/1809.03699/) (2018)
- [Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions](https://mlsanity.com/paper/2209.04160/) (2022)

## Abstract (arXiv)

The convergence of the Metaverse and Large Language Model (LLM)-based AI agent is catalyzing a shift toward autonomous, immersive, and personalized pedagogical frameworks in medical education. This paper presents a novel agentic AI-driven immersive simulation specifically designed for High Dose Rate (HDR) vaginal cylinder (VC) brachytherapy in cancer care. By integrating Virtual Reality (VR) and mobile computing, the system establishes a high-fidelity, risk-free environment that allows trainees to master complex procedural skills without the facility or safety constraints posed by physical anatomy or live radioactive sources. A core contribution of this work is the seamless integration of a knowledge-aware assistant leveraging Retrieval-Augmented Generation (RAG) to ground agent interactions in authoritative clinical guidelines. This architecture also enables an interactive agent to provide natural language interfaces and hands-free, real-time guidance during intricate medical maneuvers. We validate the proposed system through a prototype deployment comprising a Meta Quest 3 interface linked to a local GPU-accelerated AI backend, demonstrating a feasible architecture for HDR brachytherapy simulation. Experimental results indicate that the system maintains suitable end-to-end latency and high context precision, answer completeness, and relevance in the RAG-enhanced pedagogical support.