对语言模型和护士在提供新生儿临床决策支持方面的能力进行评估
Chedva Levin1, Tehilla Kagan2, Shani Rosen3
1Faculty of School of Life and Health Sciences, Nursing Department, The Jerusalem College of Technology-Lev Academic Center, Jerusalem, Israel; The Department of Vascular Surgery, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Tel Aviv, Israel.
International journal of nursing studies
|April 30, 2024
概括
大型语言模型显示出新生儿护理的潜力,但需要改进. 克劳德2.0在准确性和速度上优于ChatGPT-4,但在临床集成之前,这两种模型都需要改进.
科学领域:
- 医疗保健中的人工智能
- 新生儿临床推理 新生儿临床推理
- 临床决策支持系统 临床决策支持系统
背景情况:
- 人工智能 (AI) 在临床实践中的整合为改善医疗保健提供提供了潜力.
- 评估先进的人工智能模型的临床推理能力,例如大型语言模型 (LLM),对于评估它们在新生儿重症监护等专业领域的适用性至关重要.
研究的目的:
- 为了比较两个著名的LLMs,ChatGPT-4和Claude-2.0的临床推理能力,与经验丰富的新生儿护士相比.
- 在模拟的新生儿护理场景中评估AI性能,重点关注准确性,完整性和响应时间.
主要方法:
- 一项涉及32名新生儿重症监护护士和两名LLMs (ChatGPT-4,Claude-2.0) 的横截面研究.
- 使用六个标准化新生儿重症监护室临床场景进行比较评估.
- 认证的新生儿护士从业人员对AI反应 (初步评估,治疗建议) 进行了评分,以确定准确性,完整性和响应时间.
主要成果:
- 这两种AI模型都在新生儿护理场景中展示了一些临床推理能力.
- 克劳德2.0在临床准确性和响应速度方面明显优于ChatGPT-4.
- 两种模型的确定的局限性包括诊断精度,治疗特异性和响应延迟.
结论:
- 目前的LLM有前途,但在新生儿护理中临床整合之前需要大幅度改进.
- 进一步验证至关重要,以安全地利用人工智能为临床决策在这个关键的患者群体.
- 在临床应用之前,需要解决像ChatGPT-4和Claude-2.0这样的AI模型的准确性差距.
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