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

TL;DR

This paper introduces an artifact-based agent framework for medical image processing that enhances adaptability and reproducibility in clinical settings by formalizing workflows and tracking provenance.

Contribution

It presents a semantic layer and modular rule system enabling adaptive, reproducible workflows tailored to dataset-specific conditions in medical imaging.

Findings

Framework achieves adaptive configuration synthesis in clinical CT and MRI data.

Ensures deterministic reproducibility across multiple executions.

Enables semantic querying grounded in workflow artifacts.

Abstract

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.