利用大型语言模型促进心理健康：机遇、挑战与伦理考量
Harnessing Large Language Models for Mental Health: Opportunities, Challenges, and Ethical Considerations
AI总结：
本文探讨大型语言模型在心理健康护理中的变革潜力，分析其提升可及性与个性化等机遇，同时指出数据隐私、偏见等挑战，倡导创新与伦理责任并重的多学科协作路径。
摘要：
大型语言模型（LLMs）正在通过增强治疗干预中的可及性、个性化和效率，变革心理健康护理领域。这些人工智能驱动的工具为心理健康专业人员提供实时支持、改进的数据整合，并鼓励寻求护理行为，尤其是在服务不足的社区中。通过利用LLMs，从业者能够提供更具同理心、更量身定制且更有效的支持，解决心理健康服务提供中长期存在的缺口。然而，其实施伴随着重大挑战和伦理关切。性能限制、数据隐私风险、偏见输出以及生成误导性信息的可能性，凸显了制定严格伦理准则和健全评估机制的迫切需求。心理健康数据的敏感性进一步要求采取细致的保障措施，以保护患者权利并确保公平获得人工智能驱动的护理。支持者认为，LLMs有潜力使心理健康资源民主化，而批评者则警告存在滥用和减少治疗中人际联系等风险。在创新与伦理责任之间取得平衡至关重要。本文审视了LLMs在心理健康护理中的变革潜力，强调了相关的技术和伦理复杂性，并倡导采用协作性、多学科的方法，以确保这些进步与提供富有同情心、公平且有效的心理健康支持的目标相一致。
英文摘要：
Large Language Models (LLMs) are transforming mental health care by enhancing accessibility, personalization, and efficiency in therapeutic interventions. These AI-driven tools empower mental health professionals with real-time support, improved data integration, and the ability to encourage care-seeking behaviors, particularly in underserved communities. By harnessing LLMs, practitioners can deliver more empathetic, tailored, and effective support, addressing longstanding gaps in mental health service provision. However, their implementation comes with significant challenges and ethical concerns. Performance limitations, data privacy risks, biased outputs, and the potential for generating misleading information underscore the critical need for stringent ethical guidelines and robust evaluation mechanisms. The sensitive nature of mental health data further necessitates meticulous safeguards to protect patient rights and ensure equitable access to AI-driven care. Proponents argue that LLMs have the potential to democratize mental health resources, while critics warn of risks such as misuse and the diminishment of human connection in therapy. Achieving a balance between innovation and ethical responsibility is imperative. This paper examines the transformative potential of LLMs in mental health care, highlights the associated technical and ethical complexities, and advocates for a collaborative, multidisciplinary approach to ensure these advancements align with the goal of providing compassionate, equitable, and effective mental health support.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
