儿童中风中的人工智能:查中的盟友?
I Ojeda-Velázquez1, B Bermejo-González1, M García de Oteyza2
1Pediatric Medical Intern, Pediatrics Department, Hospital General Universitario Gregorio Marañón, Madrid, Spain.
概括
在诊断疑似中风或计算儿科紧急情况中的神经学尺度方面,ChatGPT的实用性有限. 然而,低中风概率得分可能有助于排除中风,这表明潜在的支持作用.
科学领域:
- 儿科急救医学 儿科急救医学
- 医疗保健中的人工智能
- 神经学 神经学
背景情况:
- 评估像ChatGPT这样的AI工具对于临床决策支持至关重要.
- 儿童中风疑似需要准确和及时的诊断.
- 标准化尺度 (GCS,PEDNIHSS) 对于神经评估至关重要.
研究的目的:
- 为了评估ChatGPT的诊断指南准确性,怀疑儿科中风.
- 评估ChatGPT预测中风可能性的能力.
- 分析ChatGPT在计算格拉斯哥昏迷量表 (GCS) 和国家儿童卫生研究所中风量表 (PEDNIHSS) 的表现.
主要方法:
- 在儿科急诊室进行了回顾性观察性研究.
- 在ChatGPT对话中使用了标准化的患者病例模板.
- 聊天GPT被提示进行诊断,中风概率,GCS和PEDNIHSS计算,结果与临床评估进行比较.
主要成果:
- 聊天GPT显示诊断指南一致性较弱 (k=0.291),但灵敏度高 (90.9%) 和特异性低 (57.4%).
- 预测中风概率的曲线下面面积 (AUC) 为0.796;得分<4显示高负预测值 (93.75%).
- 对GCS (k=0.227) 和PEDNIHSS (k=0.261) 计算的一致性很弱.
结论:
- 目前,ChatGPT不适用于在怀疑儿科中风病例的直接诊断指导或尺度计算.
- 通过ChatGPT生成的低于4的中风概率得分可能有助于排除中风.
- 聊天GPT可能会成为儿科中风代码激活的补充工具.
相关概念视频
Stereotype Content Model
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Intelligence
The term "intelligence" is complex because it refers to both behavior and individuals, and its interpretation varies across cultures. European Americans tend to link intelligence with reasoning and cognitive skills, while in Kenya, it is tied to responsible participation in family and social life. In Uganda, intelligence is seen as the ability to know the right actions and carry them out effectively, while the Iatmul people of Papua New Guinea associate it with the capacity to remember detailed...
Binet's Contribution to Measures of Intelligence
Alfred Binet, along with his student Théophile Simon, was tasked by the French Ministry of Education in 1904 to create a method for identifying students who struggled to learn through conventional classroom instruction. This initiative aimed to address overcrowding by placing such students in specialized schools. Binet and Simon developed an intelligence test comprising 30 tasks, ranging from simple commands, like touching one's nose or ear, to more complex tasks, such as drawing designs from...


