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眼科诊断和管理计划的DeepSeek-R1与OpenAI o1

David Mikhail¹, Andrew Farah², Jason Milad³

¹Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

JAMA ophthalmology

|September 4, 2025

概括

与专有模型相比,像DeepSeek-R1这样的开放式大型语言模型 (LLM) 在复杂的眼科病例中显示出更高的诊断和管理准确性,同时也降低了成本.

科学领域:

眼科眼科
人工智能
临床决策支持

背景情况:

大型语言模型 (LLM) 越来越多地被用于临床决策.
很少有研究评估了临床实践中复杂的眼科病例的LLM性能.
评估开放型与专有型LLM对于临床实用性和可访问性至关重要.

研究的目的:

为了比较DeepSeek-R1和OpenAI o1的诊断准确性和管理决策.
评估这些LLM在眼科中的成本效益.
了解在复杂的临床场景中推理增强的开放式LLM的性能.

主要方法:

通过使用标准化提示 (Plan-and-Solve Plus) 对DeepSeek-R1和OpenAI o1进行横截面评估.
在10个子专业中利用了JAMA眼科的临床挑战文章中的422个复杂的临床病例.
评估诊断准确性,下一步决策准确性和代币成本.

主要成果:

DeepSeek-R1 的诊断准确度比 OpenAI o1 (63. 0%) 高 (P=. 02).
与OpenAI o1 (75.8%) 相比,DeepSeek-R1的下一步决策准确度也更高 (82.7%).
DeepSeek-R1 的成本显著降低,节省了高达98.5%的成本.

结论:

在眼科病例的诊断和管理准确性方面,DeepSeek-R1的表现优于OpenAI.
像DeepSeek-R1这样的开放式LLM为临床决策提供了具有成本效益和可扩展性的解决方案.
需要进一步研究安全性和特定领域的适应性,以实现最佳的临床整合.

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