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Jinxiao Hou^1,2,3,4,5, Furun An^1,2,3,4,5, Hui Qin^1,2,3,4,5

¹Department of Hematology, the Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

BMC medical education

|October 2, 2025

概括

将开源大型语言模型 (LLM) DeepSeek 集成到基于问题的学习 (PBL) 中,为血液病学 residents 改善了临床推理和诊断技能. 虽然有效,但对AI准确性的担忧仍然存在,需要进一步调查.

科学领域:

医学教育医学教育
人工智能在医学中的应用
血液学培训血液学培训

背景情况:

传统的基于问题的学习 (PBL) 是医学教育的基石.
集成先进的AI工具,如大型语言模型 (LLM),为提高培训提供了新的机会.
在专业医疗课程中评估特定LLM的有效性至关重要.

研究的目的:

评估将开源的LLM DeepSeek集成到血液学住院PBL课程中的影响.
将DeepSeek辅助的PBL与传统的PBL方法的有效性进行比较.
评估居民在各种能力领域的表现.

主要方法:

一项非随机对照试验,涉及60名血液病学住院医生,分为两组.
一组接受传统的PBL;另一组接受了DeepSeek辅助的PBL (使用DeepSeek V3和R1模型与人工智能辅助搜索).
通过课程后调查和五次标准考试来衡量学习成果.

主要成果:

深度搜索辅助的PBL在病例分析,反质量和临床推理方面显示出显著的优势.
深度搜索小组在五个考试领域中的四个领域表现出色,这表明学业表现有所改善.
在临床技能方面没有发现显著差异,参与者对人工智能的准确性表示担忧.

结论:

集成DeepSeek可以提高临床能力,诊断推理和参与血液学培训.
开源的LLM提供了可扩展的,具有成本效益的工具来增强医学教育.
需要进一步的研究来改善人工智能在交互元素和程序技能培训中的作用.

关键词:

在DeepSeek搜索中深入搜索.血液学专业住院医生大型语言模型.医学教育医学教育基于问题的学习是基于问题的学习.

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