精神病学中的大型语言模型:机遇和挑战
Sebastian Volkmer1, Andreas Meyer-Lindenberg2, Emanuel Schwarz1
1Hector Institute for Artificial Intelligence in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany; Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
Psychiatry research
|June 23, 2024
概括
大型语言模型 (LLM) 提供了令人兴奋的精神病学应用,如风险预测和治疗,但挑战仍然存在. 仔细考虑LLM的局限性,偏见和隐私对于安全的临床整合至关重要.
科学领域:
- 精神病学是一个精神病学.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 展示了先进的文本分析能力.
- 它们在精神病学中的应用提供了新的机会和重大挑战.
- 法律学士在庞大的数据集上接受培训,并有可能对特定任务进行微调.
研究的目的:
- 提供精神病学LLM的全面审查.
- 探索模型架构,潜在的用例和临床考虑.
- 检查在精神病学环境中采用LLM的机会和局限性.
主要方法:
- 关于精神病学研究中的大型语言模型 (LLM) 的文献综述.
- 对LLM架构和培训方法的分析.
- 检查潜在的精神病学应用和相关风险.
主要成果:
- 在预测患者的风险因素和帮助治疗干预方面,LLM显得有前途.
- 潜在的应用包括分析治疗材料和提高患者参与度.
- 显著的挑战包括LLM偏见,隐私问题和错误信息的风险.
结论:
- 法学士课程为精神病学实践提供了变革性的潜力.
- 解决诸如偏见和隐私等局限性对于负责任的实施至关重要.
- 为了安全有效地将LLM纳入精神病学,需要进一步的研究和仔细的临床考虑.
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