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相关概念视频

Artificial Intelligence-Based System for Detecting Attention Levels in Students06:37

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This paper proposes an artificial intelligence-based system to automatically detect whether students are paying attention to the class or are distracted. This system is designed to help teachers maintain students' attention, optimize their lessons, and dynamically introduce modifications in order for them to be more...
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相关实验视频

Updated: Jan 20, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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有关心血管疾病预防的不准确信息被生成人工智能所启用.

Astefanos Al-Dalakta1, Bianca Honnekeri1, Fatima Rodriguez2

  • 1Department of Cardiovascular Medicine, Cleveland Clinic Foundation, Cleveland, OH, USA.

American journal of preventive cardiology
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

生成性AI模型可以很容易地产生不准确的心血管疾病 (CVD) 预防信息. 这项研究发现,OpenAI和DeepSeek模型都产生了不可靠的健康建议,突出了人工智能生成的医疗内容的风险.

关键词:
人工智能的人工智能是人工智能.心血管疾病是什么心血管疾病预防 预防 预防

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 预防心血管疾病 预防心血管疾病

背景情况:

  • 不准确的心血管疾病 (CVD) 预防信息的流行率在线.
  • 越来越多的人工智能 (AI) 聊天机器人用于医疗查询.
  • 错误信息对公共卫生决策的潜在影响.

研究的目的:

  • 评估由两个领先的生成性AI (genAI) 模型生成的CVD预防信息的准确性.
  • 评估 genAI 在常见的心血管疾病预防主题 (如他类药物治疗和胆固醇管理) 的表现.
  • 在中立和不准确的条件下比较来自OpenAI和DeepSeek模型的响应.

主要方法:

  • 由医生领导的实验涉及两名经过董事会认证的预防性心脏病专家.
  • 用中性和不准确的语调提示对九个常见的CVD预防主题的genAI反应的评估.
  • 根据内容和引用,对答案进行适当,边界或不适当的分类.

主要成果:

  • 开放AI: 88.9%适用于中立提示; 0%适用于不准确提示 (77.8%不适合).
  • DeepSeek-R1: 66.7%适用于中性提示;100%不适用于不准确提示.
  • 两种模型都表明,在被提示时产生不准确的心血管疾病预防信息的倾向.

结论:

  • 生成型人工智能模型可以很容易地被提示产生不准确的心血管疾病预防信息.
  • 与人工智能驱动的健康信息相关的重大风险需要进一步的研究和政策干预.
  • 需要对人工智能产生的医疗内容进行警和批判性评估,以确保公共卫生安全.