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Prompt engineering shows mixed results for LLMs in clinical decision-making

By PulseAugur Editorial · [1 sources] ·

A new study published on arXiv has investigated the effectiveness of prompt engineering for improving Large Language Model (LLM) performance in clinical decision-making tasks. Researchers evaluated three leading LLMs—ChatGPT 4o, Gemini 1.5 Pro, and LIama 3.3 70B—across various stages of patient care, including differential diagnosis, immediate steps, diagnostic testing, final diagnosis, and treatment recommendations. The findings indicate that while LLMs can achieve high accuracy in final diagnoses, their performance varies significantly across different tasks, and prompt engineering does not offer a universal solution, sometimes even proving counterproductive. AI

IMPACT Prompt engineering's effectiveness is highly model and task-dependent, suggesting a need for tailored strategies when integrating LLMs into healthcare.

RANK_REASON Academic paper detailing research findings on LLM performance. [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 →

Prompt engineering shows mixed results for LLMs in clinical decision-making

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Academic paper detailing research findings on LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Mengdi Chai, Ali R. Zomorrodi ·

    Prompt engineering does not universally improve Large Language Model performance across clinical decision-making tasks

    arXiv:2512.22966v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated promise in medical knowledge assessments, yet their practical utility in real-world clinical decision-making remains underexplored. In this study, we evaluated the performance of th…

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