Tag-based few-shot learning boosts LLM accuracy in medical incident analysis

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 1 source

Researchers have developed a new method for improving the accuracy of Large Language Models in healthcare by using tag-based example selection for few-shot learning. This approach was tested on the Japanese Medical Incident Dataset, which contains over 3,800 reports of medical accidents and near-misses. Experiments using GPT-4o and LLaMA 3.3 demonstrated that the tag-based strategy significantly enhances the precision and stability of generating causal factors and preventive measures compared to random or similarity-based selection, reducing unintended outputs and safety filter activations. AI

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

IMPACT Enhances LLM reliability in high-stakes domains like healthcare, improving clinical insight generation from incident reports.

RANK_REASON The cluster contains an academic paper detailing a new method for few-shot learning with LLMs applied to a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 · Itsuki Noda · 2026-05-11 05:49

Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning

In high-stakes domains such as healthcare, the reliability of Large Language Models (LLMs) is critical, particularly when generating clinical insights from incident reports. This study proposes a tag-based few-shot example selection method for prompting LLMs to generate backgroun…

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Medical Incident Causal Factors and Preventive Measures Generation Using Tag-based Example Selection in Few-shot Learning

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