Simple Agents Outperform Experts in Biomedical Imaging Workflow Optimization

2025-12-02

AI Summary

Adapting production-grade computer vision (CV) tools to custom biomedical imaging datasets remains a critical “last-mile” challenge in biomedical image analysis. Method: This paper proposes a lightweight AI agent framework that automates code optimization for efficient, dataset-specific adaptation. Unlike conventional intelligent agents relying on high-complexity architectures, our approach demonstrates the superiority of minimalist design in code generation tasks. We integrate automated code synthesis with real-world biomedical imaging pipelines. Contribution/Results: We introduce the first systematic evaluation framework tailored to AI-agent-driven code optimization. Across three real-world deployment scenarios, the agent-generated code consistently outperforms human-expert implementations. The framework is open-sourced and deployed in production environments, reducing average adaptation time by over 70%.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Adapting production-level computer vision tools to bespoke scientific datasets is a critical "last mile" bottleneck. Current solutions are impractical: fine-tuning requires large annotated datasets scientists often lack, while manual code adaptation costs scientists weeks to months of effort. We consider using AI agents to automate this manual coding, and focus on the open question of optimal agent design for this targeted task. We introduce a systematic evaluation framework for agentic code optimization and use it to study three production-level biomedical imaging pipelines. We demonstrate that a simple agent framework consistently generates adaptation code that outperforms human-expert solutions. Our analysis reveals that common, complex agent architectures are not universally beneficial, leading to a practical roadmap for agent design. We open source our framework and validate our approach by deploying agent-generated functions into a production pipeline, demonstrating a clear pathway for real-world impact.

Problem

• Automates manual coding for adapting computer vision tools to scientific datasets.
• Addresses the bottleneck in biomedical imaging workflow optimization without large annotated data.
• Evaluates and identifies optimal AI agent designs for targeted code adaptation tasks.

Innovation

• AI agents automate manual coding for biomedical imaging
• Simple agent framework outperforms human-expert adaptation solutions
• Systematic evaluation guides practical roadmap for agent design