打破骨头,打破障碍:ChatGPT,DeepSeek和双子座在手部骨折管理中
Gianluca Marcaccini1, Ishith Seth2, Yi Xie2
1Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy.
Journal of clinical medicine
|March 27, 2025
概括
大型语言模型 (LLM) 显示了手部骨折诊断的潜力,ChatGPT-4o实现了高精度. 然而,由于人工智能的局限性,人类专业知识仍然对复杂的创伤管理至关重要.
科学领域:
- 整形外科手术 整形外科手术
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 手部骨折管理要求高诊断准确度和复杂的决策.
- 人工智能 (AI) 和大型语言模型 (LLM) 在临床工作流程中提供了潜在的帮助.
- 这项研究将人工智能模型的表现与经验丰富的外科医生在诊断和管理手部骨折方面的表现进行比较.
研究的目的:
- 为了评估ChatGPT-4o,DeepSeek-V3和双子座1.5的手部骨折的诊断和治疗建议的准确性.
- 将人工智能生成的管理计划与经验丰富的骨科外科医生的管理计划进行比较.
- 评估LLM在协助手部创伤临床决策方面的潜力.
主要方法:
- 追溯分析58个匿名手部骨折病例.
- 将临床细节 (骨折部位,位移,软组织参与) 输入到AI模型中.
- 将AI建议与实际外科医生的决定进行比较,使用准确度,精度,回忆和F1得分指标.
主要成果:
- 聊天GPT-4o实现了最高准确度 (98.28%) 和回忆 (91.74%),精度较低 (58.48%).
- DeepSeek-V3 显示了中等准确度 (63.79%) 与平衡的精度和回忆.
- 双子1.5显示性能差 (准确率为18.97%),表明临床决策支持的显著局限性.
结论:
- 人工智能模型,特别是ChatGPT-4o,在改善手部骨折管理的临床工作流程方面表现有前途,特别是在解释和分类方面.
- 人工智能表现的局限性强调了对复杂的手部创伤病例的人类专业知识的持续需求.
- 在将人工智能广泛应用于医疗决策之前,必须考虑道德考虑,包括偏见和透明度.
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