在巴西公共卫生中对人工智能的LLM辅助范围审查:从资源有限的环境中转移和联合学习中吸取教训
Fabiano Tonaco Borges1,2, Gabriela do Manco Machado3, Maíra Araújo de Santana1
1Department of Biomedical Engineering, Geoscience and Technology Center, Federal University of Pernambuco (UFPE), Recife 50740-550, PE, Brazil.
International journal of environmental research and public health
|January 28, 2026
概括
巴西医疗保健中的人工智能 (AI) 使用转移学习 (TL) 和联合学习 (FL) 实现数据隐私和资源效率. 然而,这些先进的AI方法在实践中尚未得到广泛采用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 全球卫生技术 全球卫生技术
背景情况:
- 人工智能 (AI) 是全球健康的关键技术,特别是在气候变化和数字沟方面.
- 医疗保健中的AI应用对于应对医疗系统挑战越来越重要.
- 巴西的医疗保健景观为人工智能集成提供了独特的机会和挑战.
研究的目的:
- 对巴西医疗保健中的AI应用进行范围审查.
- 在这种情况下,专门分析转移学习 (TL) 和联合学习 (FL) 的使用情况.
- 了解TL和FL如何解决人工智能医疗解决方案中的数据稀缺性,隐私和技术依赖.
主要方法:
- 进行了全面的范围审查,搜索了PubMed,SciELO和CNPq等数据库.
- 使用人工智能辅助的标题选与手动验证.
- 主题分析侧重于人工智能应用的方法和基础设施方面.
主要成果:
- 在349项检索的研究中,只有6项明确使用转移学习 (TL) 或联合学习 (FL).
- TL和FL经常在多国研究联盟中实施,突出了它们的协作潜力和可扩展性.
- 尽管它们对保护隐私和资源效率的AI部署有好处,但这些方法在巴西主流医疗保健中仍未得到充分利用.
结论:
- 包括TL和FL在内的资源意识AI在巴西卫生部门的整合还处于起步阶段,并且不均.
- 这些人工智能方法为全球南方的卫生系统提供了公平的创新和数字自主性的巨大潜力.
- 进一步采用TL和FL对于推进AI能力至关重要,同时尊重数据主权和解决数字不平等问题.
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