人工智能与医疗专业人员在昏迷患者中的临床决策
Steven van Zanten1, Thomas T Boel2, Jelle Sy de Jong2
1Reinier de Graaf Gasthuis, Department of Cardiology, Delft, the Netherlands.
JACC. Advances
|December 20, 2025
概括
生成式预训练变压器4-Omni (GPT-4o) 在昏睡转诊方面实现了100%的诊断产量,但诊断精度得分 (DPS) 较低. 虽然GPT-4o还没有准备好用于无监督的临床使用,但它发现的诊断比医疗专业人员更多.
科学领域:
- 心脏病学 心脏病学
- 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:
- 人工智能 (AI) 具有提高诊断准确度和同步评估产出的潜力.
- 目前对于昏迷的诊断途径可能是复杂和耗时的.
研究的目的:
- 为了比较生成预训练变压器4-Omni (GPT-4o) 与医疗专业人员 (MP) 在诊断昏迷中的诊断能力.
- 根据推信评估昏迷诊断和推干预措施的准确性.
主要方法:
- 这是一项三阶段研究,涉及55封匿名昏迷推信.
- GPT-4o和18个MP产生了差异诊断.
- 患者的随访和多学科的判断决定了最终的诊断.
- 使用定制的诊断精度得分 (DPS) 量化诊断性能.
主要成果:
- GPT-4o实现了100%的诊断产量,超过了医生 (81.9%) 和相关专业人员 (84.5%).
- 与医生 (48.6%) 和相关专业人士 (36.7%) 相比,GPT-4o显示出更高的诊断性能 (75.9%).
- 尽管产量更高,但GPT-4o的DPS (-6.9%) 比医生 (22.9%) 和相关专业人士 (12.6%) 低,表明精度较低.
结论:
- GPT-4o成功地为所有昏迷病例提出了诊断,突出了其在诊断产量方面的潜力.
- 然而,GPT-4o的低DPS和特定诊断错误 (例如,错误分类心脏) 表明它还不适合在解释推信中的无监督临床应用.
- 在将像GPT-4o这样的AI工具集成到临床昏迷管理中之前,需要进一步的细化和验证.
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