Mar 10, 2025

Medical Agent Controller

Quentin Marquet, Ouafae Moudni, Shakthivel Murugavel, Xavier Charles, Elise Racine

Details

Details

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Summary

The Medical Agent Controller (MAC) is a multi-agent governance framework designed to safeguard AI-powered medical chatbots by intercepting unsafe recommendations in real time.

It employs a dual-phase approach, using red-team simulations during testing and a controller agent during production to monitor and intervene when necessary.

By integrating advanced medical knowledge and adversarial testing, MAC enhances patient safety and provides actionable feedback for continuous improvement in medical AI systems.

Cite this work:

@misc {

title={

Medical Agent Controller

},

author={

Quentin Marquet, Ouafae Moudni, Shakthivel Murugavel, Xavier Charles, Elise Racine

},

date={

3/10/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Review

Review

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Reviewer's Comments

Reviewer's Comments

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Ziba Atak

Strengths:

-Comprehensive Literature Review: The paper demonstrates a strong understanding of existing literature, particularly around AI hallucinations, biases, and medical AI risks.

-Novel Methodology: The multi-agent controller framework is innovative and addresses a critical gap in regulating medical AI chatbots.

-Clear Methodology: The experimental design and methodology are well-documented, with detailed explanations of the red team and controller agents.

-Real-World Impact: The framework was tested in a controlled production environment, demonstrating its practical applicability and low-latency performance.

-Societal Relevance: The paper effectively connects AI safety challenges to real-world implications in healthcare, such as patient safety and ethical concerns.

Areas for Improvement:

-Limitations and Negative Consequences: The paper does not explicitly discuss the limitations of the framework or potential negative consequences of its implementation. Adding this would strengthen the analysis.

-Conclusion Expansion: The conclusion could be expanded to include more actionable recommendations and future research directions, particularly around scalability and cross-cultural applicability.

Suggestions for Future Work:

-Conduct larger-scale experiments to validate the findings and improve generalizability.

-Explore cross-disciplinary approaches (e.g., ethics, policy) to broaden the societal impact of the research.

-Investigate the framework’s performance in diverse cultural and regulatory contexts.

C1:

4.8

C2:

4.5

C3:

4.5

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.