A Multi-Assessment and Multi-Professional Agents Approach for Medical Chatbot Risk Estimation: A Development and Evaluation Study (Preprint)

Abstract

<sec> <title>BACKGROUND</title> Assessing chatbot responses across three domains: medical, ethical, and legal, is an essential task in ensuring the safe use of AI in healthcare. While advancements in the use of LLMs show significant improvements in evaluating question-answer datasets through multiple-choice medical exams, existing systems utilize general LLMs without applying specialized domain knowledge, relying on standardized instructions without integrating real-world information and implementing ensemble methods such as majority voting failing to resolve the disagreement with other agents, resulting in misclassification and challenges in assessing risks. </sec> <sec> <title>OBJECTIVE</title> This study aims to design, develop, and evaluate a synergistic approach for assessing risks associated with chatbot responses using multi-assessment and multi-professional agents. </sec> <sec> <title>METHODS</title> We designed and developed an approach that consists of a multi-assessment, multi-professional agent approach, specifically Initial Assessment (MA1), which internalizes three roles and provides an initial risk estimation; Final Assessment (MA3), which aims to reach a final consensus based on the previous assessments (MA1 and MA2), with each utilizing one LLM. Verification Assessment (MA2) incorporates a multi-professional agent for each risk domain (medical, ethical, legal). The proposed approach was evaluated using different systems: baseline, enhanced prompt, embedding-based search, and RAG, applying various metrics such as macro F1-score, joint accuracy, and delta (Δ). </sec> <sec> <title>RESULTS</title> The proposed approach demonstrates a significant improvement over existing systems in assessing the risk of chatbot responses in the ethical risk domain with a 0.25 increase and the legal risk domain with a 0.10 increase. It indicates that the proposed approach applied in systems with external knowledge helps improve risk estimation. However, the medical domain remains a challenge but shows slight improvements with a 0.07 increase. </sec> <sec> <title>CONCLUSIONS</title> A multi-assessment and multi-professional agent approach is an effective approach for assessing risk estimation in chatbot responses. These highlight the potential use of the approach and develop a specialized LLM for more robust and contextually grounded risk estimation. </sec>

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