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Stereotype Content Model02:16

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...

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植入物规划软件程序中的人工智能细分错误:一个概述

Ghida Lawand1, Luiz Gonzaga1, Julien Issa2

  • 1Center for Implant Dentistry, Department of Oral and Maxillofacial Surgery, College of Dentistry, University of Florida, Gainesville, Florida, USA.

Clinical implant dentistry and related research
|October 7, 2025
PubMed
概括
此摘要是机器生成的。

静态计算机辅助植入体手术 (s-CAIS) 中的人工智能细分显示出希望,但面临着成像问题和算法的错误. 手动监督对于准确的数字植入物规划至关重要.

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科学领域:

  • 数字牙科数字牙科
  • 医学中的人工智能
  • 手术规划 手术规划

背景情况:

  • 静态计算机辅助植入体手术 (s-CAIS) 依赖于精确的3D解剖模型,这些模型来自CBCT和口腔内扫描.
  • 由人工智能驱动的细分提供了自动化模型创建和减少手动细分工作负载的潜力.
  • 目前的人工智能细分面临技术和算法限制,影响准确性.

研究的目的:

  • 评估人工智能细分精度和牙科植入物规划软件的局限性.
  • 确定常见的细分错误来源及其临床影响.
  • 探索在s-CAIS工作流程中减轻细分错误的策略.

主要方法:

  • 叙事文学审查和教育实践概述.
  • 对细分错误模式的定性分析 (边界,过度/不足细分,错误识别,部分体积效应).
  • 在四个植入物规划系统 (coDiagnostiX,BlueSkyPlan,Atomica,Relu) 中展示编辑功能.

主要成果:

  • 人工智能细分错误源于成像工件,运动模糊,解剖学变异性和算法偏差.
  • 错误可能导致不准确的植入物定位,损害的外科手术指南和临床并发症.
  • 尽管人工智能进步,但手动干预至关重要;平台变化和有限的编辑工具带来了挑战.

结论:

  • 细分错误仍然是s-CAIS中的一个重要障碍,影响数字植入物规划的安全性和有效性.
  • 改进需要增强的成像协议,精细的算法和强大的临床医生监督.
  • 监管透明度和标准化验证对于在植入物外科手术中推进人工智能至关重要.