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

arXiv: https://arxiv.org/abs/2510.09308 · HTML: https://mlsanity.com/paper/2510.09308/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 10 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems Engineering](https://mlsanity.com/paper/2307.04599/) (2023)
- [AI Model Passport: Data and System Traceability Framework for Transparent AI in Health](https://mlsanity.com/paper/2506.22358/) (2025)

## Abstract (arXiv)

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.