A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
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 support multiple plausible answers depending on undisclosed factors such as symptoms, diagnoses, medications, allergies, or dietary restrictions. A language model answering such a query directly may therefore rely on unsupported assumptions about the patient. We introduce a knowledge-guided agentic framework for mitigating patient-context ambiguity before final response generation. The framework operates between the patient and an otherwise unchanged downstream languag...
| Authors | Mahyar Abbasian, Saba A. Farahani, Arshia Ilaty et al. |
|---|---|
| Primary category | cs.CL |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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