Medical Agent Controller

Well written and clear. I like the pragmatic approach of designing a tool that seems like it could be combined with an arbitrary medical chatbot.

I would be even more excited if the project tried some more original approach - I feel a bit like you are playing it safe, sticking to rather established methods and well-known challenges. That said, the problem is real and important and the project seems well executed.

Thank you for submitting your work on ‘Medical Agent Controller’ - it was interesting to read. Please see below for some feedback on your project:

1. Strengths of your proposal/ project:

- The paper is well referenced and well structured

- This is a topical area, which is in need of more attention from researchers and people in industry

- This appears to be a novel contribution

2. Areas for improvement

- This paper needs to more strongly delineate between ‘AI-driven medical chatbots’ - which are regulated as medical devices in the UK (which isn’t mentioned), and general purpose AI models that aren’t regulated as medical devices. Could you expand upon why ‘many health chatbots remain unclassified as medical devices’? (p.2).

- It is good that you provide a threat model. This could be more fleshed out (e.g., with a Theory of Change, and/or examples), and it would be helpful if you outlined the implications of these threats. E.g., what is the impact/ implication of bias and discrimination?

- More specifics would be better re: methods - e.g., ‘Through thousands of simulated conversations orchestrated by the Red Team Agent’ - how many thousand? What conversations - can you provide an example?

- It would be helpful to understand a bit more about the background of MAC - has it been used elsewhere, etc?

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.