相关实验视频
Updated: Jan 8, 2026

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Multidisciplinary Approach to Obesity Management: A Case Report
Published on: May 30, 2025
875
肥胖症中的大型语言模型:系统性审查
Thanathip Suenghataiphorn1, Narisara Tribuddharat2, Pojsakorn Danpanichkul3
1Department of Internal Medicine, Griffin Hospital, Derby, CT, USA. Thanathip.sue@gmail.com.
International journal of obesity (2005)
|December 18, 2025
概括
生成型人工智能 (AI),特别是大型语言模型 (LLM),对肥胖管理有希望,但需要进一步研究. 目前的LLM提供潜在的好处,但在准确性和一致性方面存在局限性,需要临床医生的监督.
科学领域:
- 医疗保健中的人工智能
- 肥胖管理技术 肥胖管理技术
- 临床决策支持系统 临床决策支持系统
背景情况:
- 生成型人工智能和大型语言模型 (LLM) 为应对复杂的肥胖挑战提供了新的机会.
- 人工智能的快速发展需要全面了解其在肥胖护理中的当前作用.
研究的目的:
- 系统地审查和综合关于肥胖管理中的大型语言模型 (LLM) 应用的研究.
- 批判性地评估LLMs在这个领域的性能,局限性和未来的研究方向.
主要方法:
- 在MEDLINE,OVID和SCOPUS数据库中进行系统的文献搜索,截至2025年8月.
- 包括关于LLM在医疗和外科肥胖护理中的应用研究.
- 使用ROBINS-I.I.进行发现和偏差风险评估的叙述综合.
主要成果:
- 包括33项研究,探索LLM在个性化营养,教育,医疗治疗和减肥策略中的使用.
- 在某些领域,LLM表现出有希望的准确性和实用性,但观察到显著的变化和局限性.
- 确定的局限性包括不一致的建议,不准确性,复杂案件的挑战和潜在的偏见.
结论:
- 生成性AI和LLM具有改善肥胖管理的巨大潜力,包括个性化干预和临床决策支持.
- 目前的LLM技术在准确性,一致性和处理细微的临床情况方面存在关键限制,需要临床医生持续监督.
- 进一步的研究是必要的,以提高模型培训,验证现实世界的表现,并解决临床实施的伦理问题.
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