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<p>Access to trustworthy artificial intelligence (AI) for clinical applications is uneven, especially in low-resource settings with limited and inconsistent data. Models from high-resource settings often fail to generalize. Transfer learning (TL) can adapt established models to new settings. Using neurological outcome prediction for out-of-hospital cardiac arrest (OHCA) as a proof of concept, we adapted a model trained on a large cohort to Vietnam (243 patients) and Singapore (15,916 patients) using the Pan-Asian Resuscitation Outcomes Study registry. The external model performed poorly on the Vietnam cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.467 (95% CI: 0.141–0.785), but TL markedly improved performance (AUROC = 0.807, 95% CI: 0.626–0.948). In Singapore, TL yielded modest gains (AUROC = 0.955 vs. 0.945). These findings highlights the potential of TL to improve prediction accuracy across diverse healthcare contexts and to support equitable and safe global AI adoption.</p>

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Leveraging AI and transfer learning to enhance out-of-hospital cardiac arrest outcome prediction in diverse setting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Siqi Li,
  • Yohei Okada,
  • Wenjun Gu,
  • Michael Hao Chen,
  • Son Ngoc Do,
  • Quyet Dinh Pham,
  • Quoc TA Hoang,
  • Marcus Eng Hock Ong,
  • Nan Liu,
  • Michael Y. C. Chia,
  • Yih Yng Ng,
  • Benjamin S. H. Leong,
  • Han Nee Gan,
  • Desmond R. Mao,
  • Wei Ming Ng,
  • Nausheen E. Doctor,
  • Ling Tiah,
  • Andrew F. W. Ho,
  • Wei Ling Tay,
  • Si Oon Cheah,
  • Shun Yee Low,
  • Lai Peng Tham,
  • Shir Lynn Lim,
  • Dai Quoc Khuong,
  • Long Hoang Le,
  • Tuan Anh Nguyen,
  • Chinh Quoc Luong,
  • Thang Xuan Vu,
  • Dat Tuan Nguyen,
  • Huan Huu Nguyen,
  • Hung Quang To,
  • Hai Minh Truong,
  • Hung Trong Nguyen,
  • Trang Thuy Nguyen

摘要

Access to trustworthy artificial intelligence (AI) for clinical applications is uneven, especially in low-resource settings with limited and inconsistent data. Models from high-resource settings often fail to generalize. Transfer learning (TL) can adapt established models to new settings. Using neurological outcome prediction for out-of-hospital cardiac arrest (OHCA) as a proof of concept, we adapted a model trained on a large cohort to Vietnam (243 patients) and Singapore (15,916 patients) using the Pan-Asian Resuscitation Outcomes Study registry. The external model performed poorly on the Vietnam cohort, with an area under the receiver operating characteristic curve (AUROC) of 0.467 (95% CI: 0.141–0.785), but TL markedly improved performance (AUROC = 0.807, 95% CI: 0.626–0.948). In Singapore, TL yielded modest gains (AUROC = 0.955 vs. 0.945). These findings highlights the potential of TL to improve prediction accuracy across diverse healthcare contexts and to support equitable and safe global AI adoption.