MedBuild AI: An Agent-Based Hybrid Intelligence Framework for Reshaping Agency in Healthcare Infrastructure Planning through Generative Design for Medical Architecture
🤖 AI Summary
Global healthcare infrastructure is severely unevenly distributed, with remote and underserved regions lacking access to basic medical services; conventional planning methods fail to address the scale and urgency of this challenge. This study proposes an agent-based hybrid intelligence framework that integrates large language models (LLMs) with deterministic rule engines, implementing a tri-agent collaborative system—comprising demand elicitation, rule-based inference, and 3D generative design—to enable natural-language health-need articulation, automated functional layout translation, and climate- and resource-constrained adaptation in a closed-loop design workflow. A lightweight web platform, deployed via satellite internet, supports real-time, multilingual solution generation under low-bandwidth conditions. Empirical evaluation demonstrates that the system generates code-compliant healthcare facility designs within minutes, reducing preliminary planning time by over 80%. Feasibility and improved accessibility have been validated across multiple low-resource settings.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Addresses global healthcare infrastructure disparities through generative design
Integrates AI with expert systems to improve early planning stages
Enables remote communities to access modular medical building guidance
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrates large language models with expert systems
Uses three specialized agents for design process
Generates architectural layouts and 3D models
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