An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing

Computer Science > Artificial Intelligence [Submitted on 31 Mar 2026] An Artifact-based Agent Framework for Adaptive and Reproducible Medical Image Processing Lianrui Zuo, Yihao Liu, Gaurav Rudravaram, Karthik Ramadass, Aravind R. Krishnan, Michael D. Phillips, Yelena G. Bodien, Mayur B. Patel, Paula Trujillo, Yency Forero Martinez, Stephen A. Deppen, Eric L. Grogan, Fabien Maldonado, Kevin McGann, Hudson M. Holmes, Laurie E. Cutting, Yuankai Huo, Bennett A. Landman Medical imaging research is increasingly shifting from controlled benchmark evaluation toward real-world clinical deployment. In such settings, applying analytical methods extends beyond model design to require dataset-aware workflow configuration and provenance tracking. Two requirements therefore become central: \textbf{adaptability}, the ability to configure workflows according to dataset-specific conditions and evolving analytical goals; and \textbf{reproducibility}, the guarantee that all transformations and decisions are explicitly recorded and re-executable. Here, we present an artifact-based agent framework that introduces a semantic layer to augment medical image processing. The framework formalizes intermediate and final outputs through an artifact contract, enabling structured interrogation of workflow state and goal-conditioned assembly of configurations from a modular rule library. Execution is delegated to a workflow executor to preserve deterministic computational graph construction and provenance tracking, while the agent operates locally to comply with most privacy constraints. We evaluate the framework on real-world clinical CT and MRI cohorts, demonstrating adaptive configuration synthesis, deterministic reproducibility across repeated executions, and artifact-grounded semantic querying. These results show that adaptive workflow configuration can be achieved without compromising reproducibility in heterogeneous clinical environments. Subjects: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multiagent Systems (cs.MA) Cite as: arXiv:2604.21936 [cs.AI]   (or arXiv:2604.21936v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.21936 Focus to learn more Submission history From: Lianrui Zuo [view email] [v1] Tue, 31 Mar 2026 19:28:11 UTC (4,413 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.CV cs.MA References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)