心理健康领域AI的可扩展监督：来自治疗间歇期35万次AI辅导对话的经验
Scalable Oversight for AI in Mental Health: Lessons from 350,000 AI Coaching Conversations between Therapy Sessions
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中文总结 AI 辅助
本文基于35万次AI辅导对话经验，提出结合预防性设计、实时监控和临床评估的三层监督框架，以解决心理健康AI大规模部署中的安全挑战。
中文摘要 AI 辅助
在心理健康护理中，临床医生对每一条AI输出进行审查常被提议作为一种安全保障，但警觉研究表明这种方法在大规模下会失效，并可能反而降低安全性。基于我们在治疗间歇期部署AI辅导工具、处理超过35万次对话的经验，我们描述了如何构建一个三层人机协同（human-on-the-loop）监督框架，该框架结合了预防性设计、实时监控和持续的临床医生评估。我们展示了临床审查中的具体发现如何推动迭代改进，并为评估AI系统的心理健康专业人员提供了实用建议。
英文摘要
Clinician review of every AI output is often proposed as a safeguard in mental healthcare, but vigilance research suggests this approach fails at scale and may paradoxically reduce safety. Drawing on our experience deploying an AI coaching tool across 350,000+ conversations between therapy sessions, we describe how we arrived at a three-layer human-on-the-loop oversight framework combining preventive design, real-time monitoring, and continuous clinician evaluation. We show how specific findings from clinical review drove iterative improvements, and offer practical recommendations for mental health professionals evaluating AI systems.
发表机构
- Grow Therapy
- Stanford University School of Medicine（斯坦福大学医学院）
机构由 AI 辅助整理，请以论文原文为准。
