🤖 AI Summary
This work addresses the critical challenges of safety and reliability in clinical AI systems, which often suffer from fragile prototype architectures and a lack of holistic governance, leading to accountability gaps. To overcome these limitations, we propose “Maria,” a production-grade clinical AI platform that innovatively treats AI agents as modular units, integrating Clean Architecture with an event-driven design. The platform embeds a Human-in-the-Loop governance mechanism to serve as a continuous source of feedback for iterative improvement. Through autonomous MLOps lifecycle management, Maria ensures system maintainability, auditability, scalability, and effective human oversight. This study establishes a highly reliable and auditable reference architecture for clinical AI, offering a reusable engineering paradigm for deploying trustworthy AI systems in high-stakes domains.
📝 Abstract
The integration of Artificial Intelligence (AI) into clinical settings presents a software engineering challenge, demanding a shift from isolated models to robust, governable, and reliable systems. However, brittle, prototype-derived architectures often plague industrial applications and a lack of systemic oversight, creating a ``responsibility vacuum''where safety and accountability are compromised. This paper presents an industry case study of the ``Maria''platform, a production-grade AI system in primary healthcare that addresses this gap. Our central hypothesis is that trustworthy clinical AI is achieved through the holistic integration of four foundational engineering pillars. We present a synergistic architecture that combines Clean Architecture for maintainability with an Event-driven architecture for resilience and auditability. We introduce the Agent as the primary unit of modularity, each possessing its own autonomous MLOps lifecycle. Finally, we show how a Human-in-the-Loop governance model is technically integrated not merely as a safety check, but as a critical, event-driven data source for continuous improvement. We present the platform as a reference architecture, offering practical lessons for engineers building maintainable, scalable, and accountable AI-enabled systems in high-stakes domains.