ArticleJMIR medical informatics2026
A Multiassessment and Multiprofessional Agents Approach for Medical Chatbot Risk Estimation: Development and Evaluation Study.
Article in JMIR medical informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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5 authors.
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Abstract
backgroundAssessing chatbot responses across 3 domains-medical, ethical, and legal-is essential to ensuring the safe use of artificial intelligence in health care. Although advancements in the use of large language models (LLMs) show significant improvements in evaluating question-answer datasets, such as multiple-choice medical exams, existing systems use general LLMs without incorporating specialized domain knowledge. They rely on standardized instructions without integrating real-world information, and ensemble methods such as majority voting fail to resolve disagreements among agents, resulting in misclassification and challenges in risk assessment.
objectiveThis study aims to design, develop, and evaluate a synergistic approach for assessing risks associated with chatbot responses using multiassessment (MA) and multiprofessional agents (MPAs).
methodsWe designed and developed an approach consisting of MA and MPA, specifically initial assessment (MA1), which internalizes 3 roles and provides an initial risk estimation, and final assessment (MA3), which aims to reach a final consensus based on the previous assessments (MA1 and MA2), with each using 1 LLM. The verification assessment (MA2) incorporates an MPA or role-based LLM specialized agents for each risk domain (medical, ethical, and legal). We evaluated the proposed approach using the MedNLP-CHAT (Medical Natural Language Processing for AI Chat) corpus (N=226; 100 train, 126 test), covering baseline, enhanced prompt, embedding-based search, and retrieval-augmented generation (RAG). Primary metrics included macro F
resultsThe MA-MPA framework integrated with RAG achieved the highest average macro F
conclusionsThe MA-MPA approach shows potential for improving risk estimation in chatbot responses. The results suggest that the framework is particularly useful for enhancing balanced overall performance, especially when combined with external knowledge, although the medical risk domain remains challenging. Furthermore, more specialized LLMs may further improve contextually grounded risk estimation.
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