大型语言模型在识别儿童焦虑症复杂病例方面表现优于全科医生
Inbar Levkovich1, Eyal Rabin2, Michal Brann3
1The Faculty of Education, Tel Hai College, Upper Galilee, Israel.
Digital health
|December 17, 2024
概括
人工智能 (AI) 语言模型在诊断儿童焦虑方面表现有希望,表现优于全科医生 (GP). 像Claude.AI和Gemini这样的特定AI工具显示了儿童焦虑障碍的优异识别率.
科学领域:
- 儿科心理健康 儿科心理健康
- 医学中的人工智能
- 诊断工具 诊断工具 诊断工具
背景情况:
- 儿童焦虑是常见的,但由于身体症状和并发症,经常没有被诊断出来.
- 医生在识别儿童焦虑时面临挑战,即使有有效的治疗方法.
- 这突显了儿童心理健康中需要改进的诊断方法的需要.
研究的目的:
- 评估人工智能 (AI) 语言模型 (LLM) 在识别儿童焦虑方面的诊断效果.
- 为了比较LLMs与全科医生 (GPs) 在诊断儿童焦虑障碍方面的表现.
- 探索AI在解决儿科焦虑的诊断挑战方面的潜在作用.
主要方法:
- 在2024年2月,评估了多个大型语言模型 (LLM),包括ChatGPT-3.5,ChatGPT-4,Claude.AI和Gemini.
- 人工智能模型识别儿童焦虑障碍的能力与全科医生的诊断报告进行了比较.
主要成果:
- 与全科医生相比,人工智能工具的识别儿童焦虑率显著更高.
- 克劳德.AI和双子号至少发现了四起焦虑病例,ChatGPT-3发现了三起,ChatGPT-4发现了一到两起.
- 人工智能工具通常推专门的转诊,与全科医生偏好实践管理形成鲜明对比.
结论:
- 初步发现表明,LLM,特别是Claude.AI和Gemini,对儿童焦虑具有显著的诊断能力.
- 人工智能语言模型显示,在诊断儿童焦虑时,与全科医生相比,AI语言模型具有潜在的比较优势.
- 需要进一步的研究来将人工智能纳入临床实践,以改善儿科心理健康诊断.
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