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

A multi-institutional research team from Vanderbilt University Medical Center, Harvard Medical School, and others has published a paper on arXiv introducing an artifact-based agent framework designed to make medical image processing both adaptive and reproducible in real-world clinical settings. The framework tackles two critical requirements for clinical deployment: adaptability—configuring workflows to dataset-specific conditions and evolving analytical goals—and reproducibility—ensuring all transformations and decisions are recorded and re-executable. It introduces a semantic layer that 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 handled by a workflow executor that preserves deterministic computational graph construction and provenance tracking, while the agent operates locally to comply with most privacy constraints.

The researchers evaluated the framework on real-world clinical CT and MRI cohorts, demonstrating three key capabilities: adaptive configuration synthesis that adjusts to dataset-specific needs, deterministic reproducibility across repeated executions, and artifact-grounded semantic querying that allows users to ask questions about the processing pipeline. This work directly addresses the shift in medical imaging from controlled benchmark evaluation toward real-world clinical deployment, where analytical methods must extend beyond model design to include dataset-aware workflow configuration and provenance tracking. The framework shows that adaptive workflow configuration can be achieved without compromising reproducibility in heterogeneous clinical environments, a significant step for deploying AI in medical imaging where data variability and regulatory requirements are major challenges.