在临床指南中对退行性脊柱疾病的检索增强生成和大语言模型的评估
Audrey Y Su1, Ashley Knebel1, Andrew Y Xu1
1Warren Alpert Medical School at Brown University, Providence, USA.
概括
笔记本LM,一个检索增强的大型语言模型 (RAG-LLM),在退行性脊髓疾病指南中,与ChatGPT-4o相比,显示出更高的准确性和基于证据的响应. 医生应该谨慎地整合人工智能工具用于临床决策.
科学领域:
- 人工智能在医学中的应用
- 脊柱外科手术 脊柱外科手术
- 临床决策支持 临床决策支持
背景情况:
- 退行性脊柱疾病需要复杂的,个性化的治疗计划.
- 将人工智能 (AI) 整合到脊椎疾病的临床实践中,带来了独特的挑战.
- 之前的研究已经评估了像ChatGPT-4o这样的AI模型,用于特定的脊柱状况.
研究的目的:
- 这项研究比较了传统的大型语言模型 (LLM),ChatGPT-4o,与检索增强模型 (RAG-LLM),NotebookLM.o的性能.
- 该比较的重点是与北美脊柱协会 (NASS) 对退行性脊柱疾病的指南的一致性.
- 这项研究评估了人工智能模型在五个NASS指南中的性能.
主要方法:
- 使用了来自NASS指南的118个问题,涉及5种退行性脊柱疾病.
- 这些问题出现在ChatGPT-4o和NotebookLM.
- 对答案的准确性,基于证据的结论,信息的完整性和补充性进行了评估.
主要成果:
- 在准确性 (98.3%对40.7%),基于证据的结论 (99.1%对40.7%) 和完整性 (94.1%对79.7%) 方面,笔记本LM显著超过了ChatGPT-4o.
- 聊天GPT-4o提供了更多的补充信息 (98.3%与67.8%相比),但通常是毫无根据的确定性,特别是在干预方面.
- 在非手术和手术治疗的建议中,差异最为明显.
结论:
- 检索增强的大型语言模型 (RAG-LLMs) 在脊柱护理中的临床决策支持方面显示出重大前景.
- 尽管有进展,但医生必须在患者护理中实施AI时保持谨慎,特别是在复杂的医疗场景中.
- 需要进一步的研究来完善人工智能工具,以便在脊柱外科手术等专业领域可靠的临床整合.
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