Pilot Assessment of Transparency of LLM-based Systems to Support Emergency Rooms

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。

Original abstract

One of the main challenges when developing medical decision support systems for the emergency room is adequately filtering the most relevant information. High workload, stress, and the necessity for urgent decisions require precise answers to the questions posed. Although LLM-based systems can provide abundant information, physicians need concise and relevant data in this particular clinical setting. In this study, we perform a pilot assessment of the transparency of selected LLM-based systems. The comparative analysis includes ChatGPT o1 model, which was asked to produce responses with varying temperatures and a pilot graph-based RAG specializing in cardiovascular diseases. A survey was conducted among 33 clinicians regarding the amount of information contained in the provided prompts. Physicians favored the most readable, specific, and helpful answers in emergency department conditions. Reliable medical data and the form in which answers are delivered are crucial for physicians working in the emergency room. We conclude that physicians have preferences for LLM responses at a specific temperature. Further research should be expanded to enable tailoring responses not only to the clinical situation but also to the experience of the asking physician.

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