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

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

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

排序理由 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]

在 arXiv cs.CL 阅读 →

• BioBERT
• Claude Haiku 4.5
• HealthCareMagic-100K corpus
• LLMs
• TF-IDF

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