探索抑郁症大语言模型的有效性和潜力:系统性审查
Mahmud Omar1, Inbar Levkovich2
1Tel-Aviv University, Faculty of Medicine, Israel.
Journal of affective disorders
|November 24, 2024
概括
大型语言模型 (LLM) 在诊断和管理抑郁症方面表现有前途,特别是在早期检测和症状分类方面. 然而,将它们纳入临床实践需要进一步研究隐私和伦理方面的考虑.
科学领域:
- 人工智能在心理健康中的作用
- 临床信息学 临床信息学
- 计算精神病学是一种计算精神病学.
背景情况:
- 抑郁症是一个重大的全球公共卫生挑战.
- 现有的关于人工智能在心理健康方面的评论缺乏对大型语言模型 (LLM) 的具体关注.
- 对抑郁症LLM的实用性和临床整合需要进行批判性评估.
研究的目的:
- 系统地审查LLM在诊断和管理抑郁症方面的有用性.
- 研究将LLMs纳入抑郁症护理的临床实践.
主要方法:
- 从2018年1月到2024年3月对文献进行系统审查.
- 在PubMed,Embase,Web of Science和Scopus数据库中进行的搜索.
- 遵守PRISMA指南,包括使用ROBINS-I,QUADAS-2和PROBAST工具进行偏差风险评估.
主要成果:
- 包括34项关于LLM在抑郁症检测和分类中的应用研究,使用临床和社交媒体数据.
- 像RoBERTa和BERT这样的模型在早期检测和症状分类方面表现出高的有效性.
- 在临床实践中LLM的整合是早期阶段,与数据隐私和道德问题.
结论:
- 终身医学具有彻底改变抑郁症诊断和治疗策略的巨大潜力.
- 完全的临床整合需要严格的验证,道德监督和强有力的隐私保护措施.
- 确保在精神卫生保健中安全有效地使用LLM至关重要.
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