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评估使用生成人工智能来支持对罕见疾病的遗传咨询
Suok Jeon1, Su-A Lee1, Hae-Sun Chung2
1Department of Genetic Counseling, Graduate School, College of Medicine, Ewha Womans University, Seoul 03760, Republic of Korea.
Diagnostics (Basel, Switzerland)
|March 28, 2025
概括
生成型人工智能模型通常为罕见疾病提供可靠的信息,但不准确可能会导致患者焦虑. 专家指导对于在遗传咨询中安全有效地使用AI至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 罕见疾病信息 获取 获取
背景情况:
- 由于患病率低,罕见疾病对信息检索提出了独特的挑战.
- 患者和家人经常参与自我指导的学习,增加对错误信息的敏感性.
- 准确和可访问的信息对于患者和家人进行罕见疾病诊断和治疗至关重要.
研究的目的:
- 评估生成性人工智能 (AI) 模型在回答罕见疾病相关问题的准确性和安全性.
- 评估生成性AI在支持寻求遗传咨询的患者和家庭中的实用性.
- 为了比较四个领先的生成性AI模型在提供罕见疾病信息方面的表现.
主要方法:
- 四个生成性AI模型 (ChatGPT,Gemini,Claude,Perplexity) 进行了测试.
- 在四种罕见疾病中使用了102个问题,涵盖一般信息,诊断,治疗,预后和咨询.
- 答案由四位专家在利克尔特级别上对专业性和准确性进行了评估.
主要成果:
- 聊天GPT,双子座和克劳德模型在准确性和专业性方面获得了类似的高平均分.
- 困惑性AI模型的表现明显低于其他三种模型.
- 在AI模型中的咨询相关查询中,观察到响应准确度的显著差异.
结论:
- 生成型人工智能模型通常为罕见疾病提供可靠的信息.
- 人工智能响应中的偶尔的不准确性和模两可的引用可能会引起患者的焦虑.
- 专家指导对于确保为患者和家人提供适当和安全的生成AI使用至关重要.
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