探索人工智能模型在急诊科分诊中的应用潜力。

Fatma Tortum, Kamber Kasali
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摘要

目的对分诊护士和急诊科医生使用ChatGPT、Gemini和Pi进行的三级分诊方案进行比较分析,ChatGPT、Gemini和Pi是公认的人工智能(AI)模型,在日常生活中被广泛使用:该研究对 2024 年 4 月 1 日至 2024 年 4 月 7 日期间在一家三甲医院急诊科就诊的患者进行了前瞻性研究。在此期间,对急诊科就诊患者的主诉、动脉血压值、心率、脉搏氧饱和度测量值、体温值、年龄和性别特征等相关数据进行了分析。对分诊护士、上述人工智能聊天机器人和急诊科医生确定的分诊类别进行了比较:研究包括 500 名患者,其中 23.8%的患者被所有分诊评估人员归为相同类别。与急诊科医生的分诊相比,分诊护士多分了6.4%的患者,少分了3.1%的黄码患者和3.4%的红码患者。在人工智能聊天机器人中,ChatGPT 的分诊最接近急诊科医生的分诊;但是,它对黄码病人的分诊不足率为 26.5%,对红码病人的分诊不足率为 42.6%:结论:人工智能模型中观察到的误诊率相当高。结论:在人工智能模型中观察到的误诊率相当高,因此,在急诊科中完全依赖指定的人工智能模型进行分诊似乎还不合适。
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Exploring the potential of artificial intelligence models for triage in the emergency department.

Objective: To perform a comparative analysis of the three-level triage protocol conducted by triage nurses and emergency medicine doctors with the use of ChatGPT, Gemini, and Pi, which are recognized artificial intelligence (AI) models widely used in the daily life.

Materials and methods: The study was prospectively conducted with patients presenting to the emergency department of a tertiary care hospital from 1 April 2024, to 7 April 2024. Among the patients who presented to the emergency department over this period, data pertaining to their primary complaints, arterial blood pressure values, heart rates, peripheral oxygen saturation values measured by pulse oximetry, body temperature values, age, and gender characteristics were analyzed. The triage categories determined by triage nurses, the abovementioned AI chatbots, and emergency medicine doctors were compared.

Results: The study included 500 patients, of whom 23.8% were categorized identically by all triage evaluators. Compared to the triage conducted by emergency medicine doctors, triage nurses overtriaged 6.4% of the patients and undertriaged 3.1% of the yellow-coded patients and 3.4% of the red-coded patients. Of the AI chatbots, ChatGPT exhibited the closest triage approximation to that of emergency medicine doctors; however, its undertriage rates were 26.5% for yellow-coded patients and 42.6% for red-coded patients.

Conclusion: The undertriage rates observed in AI models were considerably high. Hence, it does not yet seem appropriate to solely rely on the specified AI models for triage purposes in the emergency department.

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