arXiv:2504.11301v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents have demonstrated strong capabilities across a wide range of tasks, and their application in the medical domain holds particular promise due to the demand for high generalizability and reliance on interdisciplinary knowledge. However, existing medical agent systems often rely on static, manually crafted workflows that lack the flexibility to accommodate diverse diagnostic requirements and adapt to emerging clinical scenarios. Motivated by the success of automated machine learning (AutoML), this paper introduces a novel framework for the automated design of medical agent architectures. Specifically, we define a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at the node, structural, and framework levels. Our framework conceptualizes medical agents as graph-based architectures composed of diverse, functional node types and supports iterative self-improvement guided by diagnostic feedback. Experimental results on skin disease diagnosis tasks demonstrate that the proposed method effectively evolves workflow structures and significantly enhances diagnostic accuracy over time. This work represents the first fully automated framework for medical agent architecture design and offers a scalable, adaptable foundation for deploying intelligent agents in real-world clinical environments.
The article “Automated Design of Medical Agent Architectures: A Hierarchical and Expressive Framework” explores the potential of Large Language Model (LLM)-based agents in the medical domain. These agents have shown impressive capabilities in various tasks and are particularly promising in healthcare due to the need for high generalizability and interdisciplinary knowledge. However, current medical agent systems often lack flexibility and struggle to adapt to diverse diagnostic requirements and emerging clinical scenarios. In response, this paper introduces a novel framework inspired by automated machine learning (AutoML) for designing medical agent architectures. This framework defines a hierarchical and expressive agent search space that allows dynamic workflow adaptation through structured modifications at different levels. The proposed method conceptualizes medical agents as graph-based architectures composed of functional node types and supports iterative self-improvement guided by diagnostic feedback. Experimental results on skin disease diagnosis tasks demonstrate the effectiveness of the approach in evolving workflow structures and significantly enhancing diagnostic accuracy over time. This work represents the first fully automated framework for medical agent architecture design and provides a scalable and adaptable foundation for deploying intelligent agents in real-world clinical environments.
Automated Design of Medical Agent Architectures
Large Language Model (LLM)-based agents have proven to be highly capable in various tasks, making them particularly promising in the medical field, where high generalizability and interdisciplinary knowledge are crucial. However, existing medical agent systems often lack the flexibility to accommodate diverse diagnostic requirements and adapt to emerging clinical scenarios, relying instead on static, manually crafted workflows.
To address this limitation, this paper introduces a novel framework for the automated design of medical agent architectures, drawing inspiration from the success of automated machine learning (AutoML). The framework defines a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at the node, structural, and framework levels.
In this framework, medical agents are conceptualized as graph-based architectures composed of diverse, functional node types. These agents support iterative self-improvement guided by diagnostic feedback. By leveraging this feedback loop, the framework can evolve workflow structures and enhance diagnostic accuracy over time.
Experimental Results
To validate the effectiveness of the proposed method, experimental results on skin disease diagnosis tasks were conducted. The results demonstrated that the automated framework for medical agent architecture design significantly improves diagnostic accuracy over time.
Implications and Significance
This work introduces the first fully automated framework for medical agent architecture design. By offering a scalable and adaptable foundation, this framework opens up possibilities for deploying intelligent agents in real-world clinical environments. The automated design allows for the development of medical agents capable of adapting to new diagnostic requirements and clinical scenarios, enhancing patient care and outcomes.
Conclusion
The development of automated machine learning techniques has paved the way for innovations in various domains, and now, the medical field can benefit from these advancements. By introducing a novel framework for the automated design of medical agent architectures, this paper demonstrates the potential to revolutionize medical diagnosis and treatment. With the proposed method, medical agents can dynamically adapt to evolving requirements and enhance diagnostic accuracy, leading to improved patient care and outcomes in real-world clinical environments.
“The automated design of medical agent architectures offers a scalable and adaptable foundation for deploying intelligent agents in real-world clinical environments.”
The paper titled “Automated Design of Medical Agent Architectures” introduces a novel framework that aims to address the limitations of existing medical agent systems. These systems, although powerful, often rely on static workflows that cannot adapt to diverse diagnostic requirements or emerging clinical scenarios. The authors propose a hierarchical and expressive agent search space that enables dynamic workflow adaptation through structured modifications at different levels.
One notable aspect of this framework is its conceptualization of medical agents as graph-based architectures composed of diverse functional node types. This approach allows for flexibility and adaptability in the agent’s structure, enabling it to evolve over time. Additionally, the framework supports iterative self-improvement guided by diagnostic feedback, which is crucial for enhancing diagnostic accuracy.
The experimental results presented in the paper, focusing on skin disease diagnosis tasks, demonstrate the effectiveness of the proposed method. The evolved workflow structures significantly improve diagnostic accuracy over time. This is a promising finding as it suggests that the automated design of medical agent architectures can lead to better performance in real-world clinical environments.
The significance of this work lies in its potential to revolutionize the field of medical agent systems. By automating the design process, this framework offers a scalable and adaptable foundation for deploying intelligent agents in healthcare settings. This could have a profound impact on medical practice, as it would enable agents to keep up with evolving diagnostic requirements and adapt to new clinical scenarios.
However, there are several considerations to keep in mind when assessing the implications of this research. Firstly, the evaluation of the framework’s performance is limited to skin disease diagnosis tasks. It would be valuable to see how the automated design approach fares in other medical domains to assess its generalizability.
Furthermore, the paper does not discuss the potential ethical implications of deploying automated medical agents. As these agents interact directly with patients and make critical decisions, ensuring transparency, fairness, and accountability in their design and operation is crucial. Future research should address these ethical concerns to ensure the responsible and ethical deployment of automated medical agent systems.
In conclusion, the automated design framework proposed in this paper represents a significant step forward in the development of intelligent medical agent systems. By enabling dynamic workflow adaptation and iterative self-improvement, this framework has the potential to enhance diagnostic accuracy and improve patient care. Further research, including evaluation in different medical domains and addressing ethical considerations, will be essential to fully realize the benefits of this approach.
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