AI 中文总结
该研究探讨与大语言模型聊天机器人相关的精神病是否应成为独立临床实体，分析其机制、利弊并提出多主体应对建议，强调需关注该现象。
AI 中文摘要
“AI 精神病”已进入公众和临床讨论，被用于描述在与基于大语言模型（LLM）的聊天机器人深度互动后出现或加重的精神病性症状，最常见的是妄想。当前证据仅限于媒体报道、个案报告和早期观察数据，但潜在暴露规模相当大，公众担忧已促使行业和监管机构作出回应。我们从临床和技术视角研究，与 AI 相关的精神病是否应被认定为独立临床实体。我们概述了提出的机制：LLM 谄媚性（即通过基于偏好的微调强化的、倾向于同意并讨好用户的特性），结合日益拟人化的设计，形成双向的“单个人的回音室”，能够放大并共同构建异常信念。随后我们权衡支持和反对其作为疾病分类学实体的论据。潜在益处包括改进病例识别、定制干预、标准化研究标准、上市后监测，以及向开发者和监管机构施压以采取行动。谨慎的理由包括：过早从轶事证据中将综合征实体化的风险、现有诊断结构是否已将 AI 使用作为促成因素纳入的可能性、该术语中未被证实的因果主张、污名化，以及以精神病为中心的标签可能掩盖与 AI 相关的更广泛心理健康损害的风险。我们以对临床医生、开发者、研究人员和监管机构的建议作结，包括精神科评估中的“技术史”、部署前针对谄媚性和妄想强化的基准测试，以及部署后监测。无论与 AI 相关的精神病是否在精神疾病分类学中占据一席之地，它所描述的现象现在就需要协调关注。
英文摘要
"AI psychosis" has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early observational data, yet the scale of potential exposure is considerable, and public concern has prompted responses from industry and regulators. We examine whether AI-associated psychosis warrants recognition as a distinct clinical entity, drawing on clinical and technical viewpoints. We outline the proposed mechanism: LLM sycophancy, a tendency to agree with and flatter users that is reinforced through preference-based fine-tuning, combines with increasingly anthropomorphic design to create a bidirectional "echo chamber of one" capable of amplifying and co-constructing unusual beliefs. We then weigh arguments for and against nosological recognition. Potential benefits include improved case identification, tailored interventions, standardised research criteria, post-market surveillance, and pressure on developers and regulators to act. Reasons for caution include the risk of prematurely reifying a syndrome from anecdotal evidence, the possibility that existing diagnostic constructs already accommodate AI use as a contributing factor, the unproven causal claim in the term itself, stigma, and the risk that a psychosis-centric label obscures a broader spectrum of AI-associated mental health harms. We conclude with recommendations for clinicians, developers, researchers, and regulators, including a "technological history" in psychiatric assessment, pre-deployment benchmarking for sycophancy and delusion reinforcement, and post-deployment surveillance. Regardless of whether AI-associated psychosis earns a place in psychiatric nosology, the phenomenon it describes demands coordinated attention now.
