[论文解读] Benefits and Harms of Large Language Models in Digital Mental Health

Munmun De Choudhury, Sachin R. Pendse+1 more|arXiv (Cornell University)|Nov 7, 2023

Digital Mental Health Interventions被引用 8

一句话总结

本篇文章通过生态框架分析大语言模型在数字心理健康中的机遇与风险，覆盖就医、社区照护、机构照护及社会照护等生态体系。

ABSTRACT

The past decade has been transformative for mental health research and practice. The ability to harness large repositories of data, whether from electronic health records (EHR), mobile devices, or social media, has revealed a potential for valuable insights into patient experiences, promising early, proactive interventions, as well as personalized treatment plans. Recent developments in generative artificial intelligence, particularly large language models (LLMs), show promise in leading digital mental health to uncharted territory. Patients are arriving at doctors' appointments with information sourced from chatbots, state-of-the-art LLMs are being incorporated in medical software and EHR systems, and chatbots from an ever-increasing number of startups promise to serve as AI companions, friends, and partners. This article presents contemporary perspectives on the opportunities and risks posed by LLMs in the design, development, and implementation of digital mental health tools. We adopt an ecological framework and draw on the affordances offered by LLMs to discuss four application areas -- care-seeking behaviors from individuals in need of care, community care provision, institutional and medical care provision, and larger care ecologies at the societal level. We engage in a thoughtful consideration of whether and how LLM-based technologies could or should be employed for enhancing mental health. The benefits and harms our article surfaces could serve to help shape future research, advocacy, and regulatory efforts focused on creating more responsible, user-friendly, equitable, and secure LLM-based tools for mental health treatment and intervention.

研究动机与目标

• 提出一个评估数字心理健康中LLMs的生态框架。
• 识别与多层生态水平相关的LLMs可用性（affordances）。
• 在四个应用领域：就医者、危机响应、临床决策支持与远程医疗未来，讨论潜在的益处与风险。
• 突出伦理、安全与公平性因素，以引导对LLMs的负责任部署。

提出的方法

• 采用基于Insel四个应用情境和四层级公共卫生模型的生态、社会生态框架。
• 将LLM的可用性映射到四个生态层级，以组织利益与风险。
• 综合来自关于LLMs在心理治疗、危机响应和临床决策支持的文献证据与示例。
• 讨论真实世界事件与监管考量，以指导负责任的设计与使用。

Figure 1: An ecological conceptualization of the use of large language models in digital mental health, based on the Social Ecological Model [ 34 ] .

实验结果

研究问题

• RQ1 在数字心理健康中，LLMs在个人、照护者、机构和社会层面可能带来哪些利益与风险？
• RQ2 如何利用或限制LLMs的可用性以在远程心理健康、危机响应、临床决策支持与心理治疗中提升护理质量并降低风险？
• RQ3 使用LLM驱动的心理健康工具会带来哪些伦理、安全与公平性问题，监管或指南如何应对？

主要发现

• LLMs在心理治疗和自我引导干预方面提供更广泛的覆盖率和可及性。
• LLMs能实现个性化和数据驱动的照护洞察，同时对治疗联盟与数据隐私提出关注。
• 危机响应应用可通过快速评估和文化定制指导获益，但存在输出不当与数据误用的风险。
• 临床决策支持能汇聚大量医学知识、辅助鉴别诊断，但可能传播错误信息或忽视心理健康的细微情境。
• 在自动化决策与数据治理方面存在显著的伦理、法律与公平性考量，需要谨慎治理与保障措施。