Abstract
Clinical Decision Support Systems (CDSS) increasingly rely on AI components to support diagnostic processes. However, existing studies primarily focus on predictive performance, while lifecycle management, reproducibility, and operational aspects of AI deployment remain less explored. This paper proposes an MLOps-based framework for managing and evaluating AI-enabled decision services within clinical information systems. The framework integrates model versioning, experiment tracking, monitoring, and lifecycle management. As a proof-of-concept implementation, a compact Vision Transformer (ViT-like) architecture was applied to brain MRI classification. Experimental results demonstrate high predictive performance (99.31\% accuracy), stable training behavior, and operational feasibility of the proposed framework. The findings highlight the importance of evaluating AI components not only in terms of accuracy but also with respect to reproducibility, traceability, and operational feasibility.
Paper Type
Poster
DOI
10.62036/ISD.2026.45
An MLOps Framework for Managing AI Components in Clinical Information Systems
Clinical Decision Support Systems (CDSS) increasingly rely on AI components to support diagnostic processes. However, existing studies primarily focus on predictive performance, while lifecycle management, reproducibility, and operational aspects of AI deployment remain less explored. This paper proposes an MLOps-based framework for managing and evaluating AI-enabled decision services within clinical information systems. The framework integrates model versioning, experiment tracking, monitoring, and lifecycle management. As a proof-of-concept implementation, a compact Vision Transformer (ViT-like) architecture was applied to brain MRI classification. Experimental results demonstrate high predictive performance (99.31\% accuracy), stable training behavior, and operational feasibility of the proposed framework. The findings highlight the importance of evaluating AI components not only in terms of accuracy but also with respect to reproducibility, traceability, and operational feasibility.
Recommended Citation
Duraj, A., Niewęgłowska, A. & Woźniak, R.(2026). An MLOps Framework for Managing AI Components in Clinical Information Systems. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.45