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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...
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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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人工智能模型应该向临床医生解释吗?

Gwénolé Abgrall1,2, Andre L Holder3, Zaineb Chelly Dagdia4

  • 1AP-HP, Service de Médecine Intensive-Réanimation, Hôpital de Bicêtre, DMU 4 CORREVE, Inserm UMR S_999, FHU SEPSIS, CARMAS, Université Paris-Saclay, 78 Rue du Général Leclerc, 94270, Le Kremlin-Bicêtre, France. gwenoleabgrall@gmail.com.

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概括
此摘要是机器生成的。

可解释的人工智能 (XAI) 对于理解重症监护中的人工智能决策至关重要. 它增强了信任,透明度和安全,尽管在定义和评估方面仍然存在挑战.

关键词:
算法偏差是一种算法偏差.临床决策的过程可解释的人工智能公平的 公平的 公平的生成型的人工智能 (GAI) 是一种人工智能.可以解释性 解释性患者的自主权患者的自主权监管合规性符合性 监管合规性透明度 透明度 透明度

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科学领域:

  • 关键护理医学 关键护理医学
  • 人工智能的人工智能是人工智能.
  • 医疗信息学 医疗信息学

背景情况:

  • 人工智能 (AI) 在重症监护决策中提供了潜在的好处.
  • 人工智能的复杂性可能会阻碍临床医生理解和采用人工智能建议.
  • 对人工智能的明确理解对于在高风险的医疗环境中有效使用至关重要.

研究的目的:

  • 强调可解释AI (XAI) 在重症监护环境中的重要性.
  • 讨论XAI在提高人工智能驱动医疗决策的信任和透明度方面的作用.
  • 确定XAI医疗保健领域的当前挑战和未来方向.

主要方法:

  • 关于可解释人工智能 (XAI) 原则和应用的文献综述.
  • 分析XAI对临床医生的信心和患者信心的影响.
  • 探索AI在重症监护中的监管和伦理考虑.

主要成果:

  • XAI提高了对人工智能决策过程的理解.
  • XAI改善了医疗保健专业人员对人工智能建议的遵守.
  • XAI有助于监管合规,并促进人工智能部署的公平性和安全性.

结论:

  • 可解释性AI (XAI) 对于将AI整合到重症监护中至关重要.
  • 解决定义和评估可解释性的挑战是XAI进步的关键.
  • 为了在医学中负责任地实施人工智能,需要平衡人工智能性能和可解释性.