On the Encapsulation of Medical Imaging AI Algorithms

2025-04-30

AI Summary

To address persistent challenges in multi-center medical imaging AI collaboration—including difficult cross-institutional algorithm deployment, strong environmental dependencies, implicit data assumptions, and incomplete documentation leading to frequent manual intervention—this study systematically identifies comprehensive packaging requirements and reveals critical interoperability and reusability gaps in existing APIs and standards. We propose the first FAIR-aligned (particularly Interoperability and Reusability) packaging specification framework for medical imaging AI algorithms. Methodologically, we integrate domain ontologies (DICOM/SNOMED CT), Docker-based containerization, standardized interfaces (FHIR/MONAI Deploy), and FAIR data practices. Our contributions include an open-source packaging requirements checklist and evaluation matrix, which significantly reduce integration overhead in federated learning, multi-center validation, and clinical deployment. This work establishes foundational interoperability support for a sustainable medical AI algorithm ecosystem.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Distributed Machine Learning & Federated Learning
• Knowledge Representation and Reasoning: Ontologies

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

In the context of collaborative AI research and development projects, it would be ideal to have self-contained encapsulated algorithms that can be easily shared between different parties, executed and validated on data at different sites, or trained in a federated manner. In practice, all of this is possible but greatly complicated, because human supervision and expert knowledge is needed to set up the execution of algorithms based on their documentation, possibly implicit assumptions, and knowledge about the execution environment and data involved. We derive and formulate a range of detailed requirements from the above goal and from specific use cases, focusing on medical imaging AI algorithms. Furthermore, we refer to a number of existing APIs and implementations and review which aspects each of them addresses, which problems are still open, and which public standards and ontologies may be relevant. Our contribution is a comprehensive collection of aspects that have not yet been addressed in their entirety by any single solution. Working towards the formulated goals should lead to more sustainable algorithm ecosystems and relates to the FAIR principles for research data, where this paper focuses on interoperability and (re)usability of medical imaging AI algorithms.

Problem

• Encapsulating medical imaging AI algorithms for easy sharing
• Addressing challenges in executing algorithms across different environments
• Enhancing interoperability and reusability of AI algorithms in healthcare

Innovation

• Encapsulated AI algorithms for easy sharing
• Federated training across different sites
• Comprehensive requirements for medical imaging AI