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了解生成人工智能,利用其潜力和最小化风险:一个视角

Tommaso Caselli1

  • 1CLCG - University of Groningen, Oude Kijk in't Jatstraat, 26, Groningen 9712EK the Netherlands.

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概括
此摘要是机器生成的。

生成性人工智能在医疗领域具有潜力,但需要仔细理解和部署作为辅助工具. 对其发展,数据和方法的批判性评估对于安全的整合至关重要.

关键词:
基金会模型 基金会模型生成性AI是一种人工智能.自然语言处理自然语言处理.

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科学领域:

  • 医疗人工智能 医疗人工智能
  • 自然语言处理自然语言处理.
  • 计算机视觉 计算机视觉

背景情况:

  • 生成性AI (GAI) 的作可能会夸大其在医学诊断中的当前能力.
  • 过去对放射学人工智能的过高估计凸显了对现实的期望的需要.
  • GAI与自然语言的相互作用需要在医疗应用中采取特定的预防措施.

研究的目的:

  • 为医学中生成人工智能提供一个现实的视角.
  • 解释生成性AI的内部工作机制.
  • 批判性地评估医疗保健中GAI的数据集和评估方法.

主要方法:

  • 对生成人工智能开发和部署原则的审查.
  • 在医疗环境中分析自然语言处理.
  • 对医疗AI的数据要求和性能指标进行批判性评估.

主要成果:

  • 生成型人工智能最好被视为需要人类监督的辅助工具.
  • 了解GAI的机制对于适当的医学应用至关重要.
  • 目前的数据集和评估方法需要仔细考虑医学中GAI.

结论:

  • 生成性人工智能可以支持医疗进步,当开发和部署负责任时.
  • 人类监督对于GAI在医疗保健中的安全和有效使用至关重要.
  • 进一步研究GAI的局限性和强有力的评估是必要的临床整合.