AI 中文总结
本研究以中国最大淋巴瘤在线社区House086为对象，分析337条ChatGPT后记录，发现社区不系统审核AI，而是将AI答案放回多判断来源，仍聚焦患者而非AI。
AI 中文摘要
患者及护理人员越来越多地使用人工智能（AI）工具解读医疗报告、权衡护理决策并寻求情感支持。然而多数研究将面向患者的AI视为用户与系统之间的私人交互。本研究利用中国最大的淋巴瘤患者及护理人员在线社区House086的数据，探究AI相关内容被用户带回同伴社区后如何被接纳。我们通过关键词搜索和人工筛选，识别了约400条2014-2026年间的公开帖子，使用受模式提示的大语言模型将其提取为结构化案例档案，并开展混合方法分析。经质量控制后，验证后的分析样本包含337条ChatGPT出现后的记录。成员最常报告使用AI获取信息支持，其次是寻求第二意见和心理社会支持。尽管成员常以积极态度引入AI，但约六分之一的人表示因AI输出而感到不堪重负。其他成员回应时，往往关注发帖者潜在的医疗或情感意图，却未涉及AI层面，即便在寻求AI输出与其他信息来源三角验证的帖子中，这种倾向依然存在。当成员确实讨论AI时，他们更多持谨慎态度而非认可。社区并未系统审核AI输出，反而更常削弱AI产生的虚假确定性，将单一的AI答案放回多种判断来源中。本研究认为，AI既未取代在线健康社区的解读工作，也未被社区系统审核，而是将意义建构的场所转移到下游，因此即便社区不关注AI，依然会关注人。
英文摘要
Patients and caregivers increasingly use artificial intelligence (AI) tools to interpret medical reports, weigh care decisions, and seek emotional support. Yet most research treats patient-facing AI as a private exchange between a user and a system. This study examines how AI-related content is taken up once users carry it back into the peer communities, using data from House086, China's largest online community for lymphoma patients and caregivers. We identified roughly 400 publicly accessible threads (2014-2026) through keyword searches and manual screening, extracted them into structured case profiles using a schema-prompted large language model, and conducted mixed-method analysis. After quality control, the verified analytic sample comprised 337 post-ChatGPT records. Members most often reported using AI for informational support, followed by second opinions and psychosocial support. Although members often introduced AI favorably, roughly one in six described feeling overwhelmed by AI output. When other members responded, they frequently engaged the poster's underlying medical or emotional intent while leaving the AI dimension unaddressed. This tendency persisted even in threads seeking triangulation between AI output and other information sources. When members did discuss the AI, they were more often cautious than endorsing. Rather than systematically auditing AI output, the community more often worked to deflate the false certainty it produced, placing a single AI answer back among multiple sources of judgment. This study argues that AI does not replace the interpretive work of online health communities, nor is it systematically audited by them. Instead, it shifts the locus of sensemaking downstream, so that the community continues to catch the person even when it does not catch the AI.
Comments20 pages, 3 figures, 8 tables including appendices
