人工智能用于诊断严重抑郁症的历史概述
Hao Liu1,2, Hairong Wu1, Zhongli Yang1
1State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Frontiers in psychiatry
|November 28, 2024
概括
人工智能 (AI) 在诊断重大抑郁症 (MDD) 方面表现有前途. 使用大型语言模型 (LLM) 的新方法旨在提高抑郁症评估的诊断准确性和可解释性.
科学领域:
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
背景情况:
- 使用人工智能对重度抑郁症 (MDD) 的自动诊断存在挑战.
- 以前的AI方法包括知识驱动 (有限的特征选择),数据驱动 (数据密集,黑子模型) 和混合方法 (改进了准确性,但解释能力较弱).
研究的目的:
- 系统地审查AI在抑郁症诊断方面的进展.
- 提出一种使用大型语言模型 (LLM) 的新方法,以提高MDD的诊断准确性和可解释性.
- 讨论LLM辅助抑郁症诊断的优点和挑战.
主要方法:
- 对AI在抑郁症诊断中的应用进行系统审查.
- 对知识驱动,数据驱动和混合人工智能方法的分析.
- 提出一种新的方法,利用LLM作为人工智能代理用于诊断援助.
主要成果:
- 第一代人工智能方法是以知识为导向的,依赖于选定的特性.
- 第二代人工智能方法是数据驱动的,需要大量数据集并提供有限的解释能力.
- 第三代人工智能方法结合了方法,提高了准确性,但保留了解释性问题.
结论:
- 目前用于MDD诊断的AI方法在准确性,数据要求和可解释性方面存在局限性.
- 大型语言模型 (LLM) 为提高人工智能辅助抑郁症诊断的准确性和可解释性提供了一个有希望的新途径.
- 这种基于LLM的方法可以为诊断MDD提供新的解决方案和前景.
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