LLMs improve outpatient referral through dynamic questioning

A new study published on arXiv explores the effectiveness of Large Language Models (LLMs) in outpatient referral processes. Researchers found that while LLMs do not significantly outperform traditional classifiers in static referral accuracy, they excel in dynamic, multi-turn dialogue scenarios. This is attributed to their ability to ask targeted follow-up questions that effectively reduce uncertainty and aid clinical decision-making. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT LLMs can enhance clinical decision-making by improving interactive diagnostic processes, moving beyond static classification.

RANK_REASON Academic paper on LLM application in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Large Language Models
• Xiaoxiao Liu

• paper
• product