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生成型人工智能能否取代疼痛研究中的现场工作?

Suzana Bojic1,2, Nemanja Radovanovic3, Milica Radovic4

  • 1Department of Anesthesiology and Intensive Care, Faculty of Medicine, University of Belgrade, Dr. Subotica 8, 11 000 Belgrade, Serbia.

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与人类报告相比,生成人工智能 (AI) 模型难以准确捕捉岩石登者的急性疼痛数据. 这些人工智能模型还产生了不可靠的,无法追踪的引用,引发了严重的数据完整性问题.

关键词:
剧烈的疼痛是急性疼痛.人工智能是一种人工智能.爬爬爬爬爬爬爬爬爬爬爬爬爬爬爬爬爬 爬爬爬爬爬 爬爬爬爬 爬爬爬爬 爬爬爬发电预训练变压器 预训练变压器

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科学领域:

  • 运动医学 运动医学
  • 人工智能的人工智能
  • 疼痛研究 疼痛研究

背景情况:

  • 生成型人工智能 (AI) 模型在疼痛研究中的数据采集方面显示出前景.
  • 人工智能产生的数据的准确性和可靠性存在担忧.
  • 这项研究将人工智能获取的岩石登者的急性疼痛数据与现场研究进行比较.

研究的目的:

  • 评估开放式生成AI模型在从岩石登者那里获取急性疼痛数据方面的能力.
  • 将人工智能产生的疼痛数据与通过传统现场研究方法收集的数据进行比较.
  • 评估AI模型在疼痛研究中的可靠性和准确性.

主要方法:

  • 52名登山者在登山期间报告了疼痛的位置和强度.
  • 五个生成预训练的变压器模型被用来回答相同的疼痛相关问题.
  • 人工智能产生的反应与登山者的自我报告数据进行了比较.

主要成果:

  • 登山者报告说,疼痛主要发生在前臂和脚上.
  • 人工智能模型确定了不同的主要疼痛部位 (手指,手,肩膀,腿,脚).
  • 两个人工智能模型提供了无法追踪的引用,表明了潜在的数据制造.

结论:

  • 目前开放的生成人工智能模型无法与该人口中现场收集的疼痛数据的质量相匹配.
  • 人工智能模型生成不存在的参考文献引发了人们对它们在研究中的可靠性的严重担忧.
  • 需要进一步开发,以提高AI在疼痛研究中的准确性和可靠性.