在一个结构化测试的诊断建议的健康表现ChatGPT
Ashwin Ramaswamy1, Alvira Tyagi2, Hannah Hugo3
1The Milton and Carroll Petrie Department of Urology, Icahn School of Medicine at Mount Sinai and Mount Sinai Health System, New York, NY, USA. ashwin.ramaswamy@mountsinai.org.
人工智能分类工具ChatGPT Health错过了52%的紧急情况,建议推迟对糖尿病酸性脂肪酸症等严重疾病的治疗. 在广泛使用之前,安全问题需要进一步验证.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 2026年1月,OpenAI推出了ChatGPT Health,作为一个消费者健康工具.
- 人工智能驱动的分拣系统越来越多地被开发用于公共用途.
研究的目的:
- 对ChatGPT健康的分拣建议进行结构化压力测试.
- 在人工智能驱动的医疗分组中识别潜在的安全问题和故障模式.
主要方法:
- 在21个临床领域进行了压力测试,使用了60个临床医生撰写的视频.
- 总共有960个响应在16个因数条件下被分析.
- 评估了定偏差和危机干预消息激活的影响.
主要成果:
- 性能显示了一个反转的U形模式,在临床极端 (非紧急和紧急) 失败.
- 该系统未能对52%的黄金标准紧急情况进行选,包括糖尿病酸性糖尿病和即将发生的呼吸衰竭.
- 定偏差显著地将分拣建议转移到不那么紧急的护理 (OR 11.7).
- 自杀思想的危机干预信息不可预测地被激活.
结论:
- 聊天GPT健康展示了重要的安全问题,包括错过的高风险紧急情况和不一致的危机干预.
- 在消费者规模部署人工智能分类系统之前,前性验证至关重要.
- 人工智能分类系统需要严格的测试,以确保患者的安全性和有效性.
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