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New framework improves healthcare chatbot accuracy by clarifying patient queries

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a knowledge-guided agentic framework designed to improve the accuracy of healthcare chatbots. This framework operates by interpreting patient queries, identifying missing context such as symptoms or medications, and asking targeted follow-up questions. By combining the original query with the acquired patient information, the framework generates a more clarified prompt for downstream language models, significantly reducing ambiguity and enhancing response accuracy. AI

IMPACT Enhances the reliability and safety of AI in healthcare by reducing errors from ambiguous patient queries.

RANK_REASON The cluster contains a research paper detailing a new framework for AI applications. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework improves healthcare chatbot accuracy by clarifying patient queries

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The cluster contains a research paper detailing a new framework for AI applications. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mahyar Abbasian, Saba A. Farahani, Arshia Ilaty, Hung Cao, Ramesh Jain, Amir M. Rahmani ·

    A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries

    arXiv:2608.19875v1 Announce Type: cross Abstract: Patients often submit short, underspecified queries to healthcare chatbots that lack the patient-specific information needed to determine an appropriate response. Although these queries may be linguistically clear, they can suppor…

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