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LLMs show promise for patient inquiry triage, but not autonomous deployment

By PulseAugur Editorial · [1 sources] ·

Researchers have explored the use of few-shot large language models for categorizing online patient inquiries, aiming to improve clinical triage. They compared prompted LLMs against traditional methods like TF-IDF and BioBERT using a constructed evaluation set. While the strongest LLM, Claude Haiku 4.5, showed improved performance over supervised baselines, it was concluded that LLMs can assist in triage prioritization and selective human review rather than autonomous deployment. AI

IMPACT LLMs can assist in prioritizing patient inquiries for clinical review, improving efficiency and safety in healthcare settings.

RANK_REASON The cluster contains an academic paper detailing a study on LLM capabilities for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

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

LLMs show promise for patient inquiry triage, but not autonomous deployment

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The cluster contains an academic paper detailing a study on LLM capabilities for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Jiafu Li ·

    Few-Shot Large Language Models for Actionable Triage Categorization of Online Patient Inquiries

    Online patient inquiries are often informal, incomplete, and written before professional assessment, yet they must still be routed to an appropriate level of clinical follow-up. We study this as a four-class actionable triage task -- self-care, schedule-visit, urgent-clinician-re…

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