# MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture

arXiv: https://arxiv.org/abs/2511.11587 · HTML: https://mlsanity.com/paper/2511.11587/

## 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 8 of its 68 references that are in the map.
- First posted on arXiv 17 Oct 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, 8 of 8)

- [BIMgent: Towards Autonomous Building Modeling via Computer-use Agents](https://mlsanity.com/paper/2506.07217/) (2025)
- [A11yShape: AI-Assisted 3-D Modeling for Blind and Low-Vision Programmers](https://mlsanity.com/paper/2508.03852/) (2025)
- [BuildingBlock: A Hybrid Approach for Structured Building Generation](https://mlsanity.com/paper/2505.04051/) (2025)
- [Zero-shot Sequential Neuro-symbolic Reasoning for Automatically Generating Architecture Schematic Designs](https://mlsanity.com/paper/2402.00052/) (2024)
- [Encoding Urban Ecologies: Automated Building Archetype Generation through Self-Supervised Learning for Energy Modeling](https://mlsanity.com/paper/2404.07435/) (2024)
- [Mediating Modes of Thought: LLM's for design scripting](https://mlsanity.com/paper/2411.14485/) (2024)
- [Evaluation of Architectural Synthesis Using Generative AI](https://mlsanity.com/paper/2503.02861/) (2025)
- [Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction](https://mlsanity.com/paper/2506.06066/) (2025)

## Abstract (arXiv)

Globally, disparities in healthcare infrastructure remain stark, leaving countless communities without access to even basic services. Traditional infrastructure planning is often slow and inaccessible, and although many architects are actively delivering humanitarian and aid-driven hospital projects worldwide, these vital efforts still fall far short of the sheer scale and urgency of demand. This paper introduces MedBuild AI, a hybrid-intelligence framework that integrates large language models (LLMs) with deterministic expert systems to rebalance the early design and conceptual planning stages. As a web-based platform, it enables any region with satellite internet access to obtain guidance on modular, low-tech, low-cost medical building designs. The system operates through three agents: the first gathers local health intelligence via conversational interaction; the second translates this input into an architectural functional program through rule-based computation; and the third generates layouts and 3D models. By embedding computational negotiation into the design process, MedBuild AI fosters a reciprocal, inclusive, and equitable approach to healthcare planning, empowering communities and redefining agency in global healthcare architecture.