用大型语言模型为双极性抑郁症提供临床决策支持
Roy H Perlis1,2, Joseph F Goldberg3, Michael J Ostacher4
1Center for Quantitative Health and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA. rperlis@mgh.harvard.edu.
概括
大型语言模型加上双相情感障碍指导方针,在支持抑郁症管理临床决策方面显示出有前途. 增强模型在50.8%的病例中与专家建议相匹配,优于非增强模型和社区临床医生.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
- 精神病学药物治疗 精神病学药物治疗
背景情况:
- 管理双极性抑郁症是复杂的,治疗指南提供了一些指导.
- 大型语言模型 (LLM) 显示出有助于临床决策的潜力.
- 目前在双相情感障碍药物治疗中的LLM应用需要进一步调查.
研究的目的:
- 评估LLM (GPT-4-turbo) 在确定双极性抑郁症最佳药物疗法的有效性.
- 为了比较LLM增强的治疗指南与非增强的LLM的性能.
- 根据专家建议和社区临床医生的准确性来评估LLM的表现.
主要方法:
- 创建了50个双极性抑郁症的临床图片.
- 双极性障碍专家确定了最佳和禁忌的药物治疗方法.
- 一个LLM (GPT-4-turbo) 经过测试,在视频上有或没有指导方针增强.
主要成果:
- 增强的LLM在50.8%的病例中确定了专家推的最佳治疗方法 (科恩的卡帕=0.31).
- 未增强的LLM实现了23.0%的准确性,明显低于增强模型.
- 增强的LLM绩效超过了社区临床医生 (23.1%的平均准确率).
结论:
- 基于证据的指导方针促使LLM提供了一个可扩展的策略,用于临床决策支持双极性抑郁症.
- 需要进一步的研究来验证疗效,并解决潜在的过度依赖和偏见.
- 增强的LLM显示有可能改善双极性抑郁症的药物治疗选择精度.
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