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<p>Artificial intelligence (AI) offers a solution to glaucoma care inequities driven by uneven resource distribution, but its real-world implementation remains limited. Here, we introduce Multi-Glau, an three-tier AI system tailored to China’s hierarchical healthcare system to promote health equity in glaucoma care, even in settings with limited equipment. The system comprises three modules: (1) a screening module for primary hospitals that eliminates reliance on imaging; (2) a pre-diagnosis module for handling incomplete data in secondary hospitals, and (3) a definitive diagnosis module for the precise diagnosis of glaucoma severity in tertiary hospitals. Multi-Glau achieved high performance (AUC: 0.9254 for screening, 0.8650 for pre-diagnosis, and 0.9516 for definitive diagnosis), with its generalizability confirmed through multicenter validation. Multi-Glau outperformed state-of-the-art models, particularly in handling missing data and providing precise glaucoma severity diagnosis, while improving ophthalmologists’ performance. These results demonstrate Multi-Glau’s potential to bridge diagnostic gaps across hospital tiers and enhance equitable healthcare access.</p>

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A three-tier AI solution for equitable glaucoma diagnosis across China’s hierarchical healthcare system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Yi Zhou,
  • Haitao Nie,
  • Xinyu Gong,
  • Minhui Dai,
  • Zhaohong Guo,
  • Xiaoling Deng,
  • Mengyang Li,
  • Yong Liu,
  • Lingyu Sun,
  • Xiangyi Tang,
  • Ling Zhou,
  • Zhiyao Tang,
  • Ziqing Xia,
  • Lemeng Feng,
  • Wulong Zhang,
  • Qingqing Yi,
  • Xiaobo Xia,
  • Bin Xie,
  • Weitao Song

摘要

Artificial intelligence (AI) offers a solution to glaucoma care inequities driven by uneven resource distribution, but its real-world implementation remains limited. Here, we introduce Multi-Glau, an three-tier AI system tailored to China’s hierarchical healthcare system to promote health equity in glaucoma care, even in settings with limited equipment. The system comprises three modules: (1) a screening module for primary hospitals that eliminates reliance on imaging; (2) a pre-diagnosis module for handling incomplete data in secondary hospitals, and (3) a definitive diagnosis module for the precise diagnosis of glaucoma severity in tertiary hospitals. Multi-Glau achieved high performance (AUC: 0.9254 for screening, 0.8650 for pre-diagnosis, and 0.9516 for definitive diagnosis), with its generalizability confirmed through multicenter validation. Multi-Glau outperformed state-of-the-art models, particularly in handling missing data and providing precise glaucoma severity diagnosis, while improving ophthalmologists’ performance. These results demonstrate Multi-Glau’s potential to bridge diagnostic gaps across hospital tiers and enhance equitable healthcare access.