A Model-Driven Engineering Approach to AI-Powered Healthcare Platforms

2025-10-10

AI Summary

Medical AI deployment faces challenges including data silos, stringent privacy regulations, and complex system engineering. To address these, this paper proposes a model-driven engineering (MDE) framework tailored for medical AI, introducing the Medical Interoperability Language (MILA)—a novel formal domain-specific language (DSL) that integrates metamodeling, DSL design, and automated code generation to enable end-to-end transformation from high-level semantic specifications to executable systems. The framework incorporates a federated learning architecture, enabling multi-institutional collaborative modeling without sharing raw patient data, thereby ensuring both semantic interoperability and privacy preservation. Evaluated in a multi-center cancer immunotherapy study, the automatically generated machine learning pipelines achieved 98.5% accuracy while reducing manual coding effort by over 70%. This work significantly enhances interoperability, reproducibility, and clinical deployment efficiency of medical AI systems.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model driven engineering (MDE) framework designed specifically for healthcare AI. The framework relies on formal metamodels, domain-specific languages (DSLs), and automated transformations to move from high level specifications to running software. At its core is the Medical Interoperability Language (MILA), a graphical DSL that enables clinicians and data scientists to define queries and machine learning pipelines using shared ontologies. When combined with a federated learning architecture, MILA allows institutions to collaborate without exchanging raw patient data, ensuring semantic consistency across sites while preserving privacy. We evaluate this approach in a multi center cancer immunotherapy study. The generated pipelines delivered strong predictive performance, with support vector machines achieving up to 98.5 percent and 98.3 percent accuracy in key tasks, while substantially reducing manual coding effort. These findings suggest that MDE principles metamodeling, semantic integration, and automated code generation can provide a practical path toward interoperable, reproducible, and trustworthy digital health platforms.

Problem

• Addressing fragmented data sources and privacy constraints in healthcare AI
• Reducing technical complexity of building reliable clinical AI systems
• Enabling cross-institution collaboration without sharing raw patient data

Innovation

• Model-driven engineering framework for healthcare AI
• Graphical DSL enabling shared ontology-based ML pipelines
• Federated learning architecture preserving patient data privacy