在生成性AI时代,重新想象患者报告的结果
Laurent Boyer1,2,3, Sara Fernandes4, Pascal Auquier4
1CEReSS, Research Centre on Health Services and Quality of Life, Aix Marseille University, Marseille, France. laurent.boyer@ap-hm.fr.
NPJ digital medicine
|October 23, 2025
概括
生成型人工智能 (AI) 可以提高患者报告的结果 (PROs),超出传统方法. 这一观点探讨了用人工智能完善当前的PRO,并开发新的语言原生工具,用于个性化医疗保健.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 患者报告的结果
背景情况:
- 传统的患者报告结果 (PROs) 受到预先定义的仪器和单维性假设的限制.
- 尽管有创新,但PRO在临床实践和人工智能 (AI) 模型中未得到充分使用.
- 现有的PRO努力捕捉患者健康体验的动态,多维性质.
研究的目的:
- 探索生成人工智能的潜力,以改变PROs的评估和实施.
- 提出将生成AI与PRO集成的两个方向:改进现有模型和开发新的母语工具.
- 确定利用人工智能的挑战和机会,以改善个人化和包容性护理的PRO.
主要方法:
- 这种观点综合了当前的文献,并提出了概念框架.
- 它将传统的,自上而下的PRO方法与自下而上的,基于叙事的生成AI方法进行对比.
- 讨论的重点是大型语言模型在灵活和上下文处理患者语言方面的能力.
主要成果:
- 生成型人工智能提供了一个从刚性,预定义的PRO工具转向灵活,叙事驱动的评估的范式转变.
- 提出了两个整合途径:增强现有的心理测量模型和开发具有颠覆性的母语工具.
- 成功实施取决于解决验证,临床可行性,公平性和信任问题.
结论:
- 生成型人工智能为克服传统PROs的局限性提供了重大机会.
- 采用人工智能驱动的,母语的方法可以带来更个性化的,有意义的,包容性的患者护理.
- 应对关键挑战对于实现人工智能的全部潜力在变革PRO方面至关重要.
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