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<p>Organoids and organ-on-a-chip (OoC) are two innovations used in biomedical applications for drug evaluation, disease modeling, and precision medicine. These microphysiological systems (MPSs), including integrated OoC, mimic the physiological or pathological conditions in human tissues or organs, overcoming the limitations of conventional cell culture and animal model experiments. The integration of artificial intelligence (AI) with MPS has further enhanced its capabilities, enabling advanced data processing, real-time monitoring, and predictive modeling. This review highlights the applications of AI-enabled organoids and OoC systems in MPS biomedical research, namely improving biomaterial development, spatial and functional optimization, growth factor combinations, and fine-tuning of microenvironmental stimuli. AI can also automate data analysis and enable real-time insights into cellular behavior in OoCs. Finally, we address the challenges and limitations associated with these MPS technologies and highlight future directions for their integration with AI.</p>

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Artificial intelligence-enabled organoids and organ-on-a-chip for pioneering biomedical platforms

  • 期刊论文

  • 发表日期：

    2026年7月31日

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  • Yixu Zhao,
  • Diantong Xie,
  • Hui Liu,
  • Bo Peng,
  • Xiaorui Li,
  • Xudong Wang,
  • Yubing Leng,
  • Jingkai Liu,
  • Tianyu Yang,
  • Hongxin Li,
  • Zizheng Deng,
  • Shiyao Chen,
  • Ruibao Chen,
  • Kairui Sun,
  • Yingran Liu,
  • Yu Wang,
  • Shengtao Zhu,
  • Fuyin Zheng,
  • Yubo Fan

摘要

Organoids and organ-on-a-chip (OoC) are two innovations used in biomedical applications for drug evaluation, disease modeling, and precision medicine. These microphysiological systems (MPSs), including integrated OoC, mimic the physiological or pathological conditions in human tissues or organs, overcoming the limitations of conventional cell culture and animal model experiments. The integration of artificial intelligence (AI) with MPS has further enhanced its capabilities, enabling advanced data processing, real-time monitoring, and predictive modeling. This review highlights the applications of AI-enabled organoids and OoC systems in MPS biomedical research, namely improving biomaterial development, spatial and functional optimization, growth factor combinations, and fine-tuning of microenvironmental stimuli. AI can also automate data analysis and enable real-time insights into cellular behavior in OoCs. Finally, we address the challenges and limitations associated with these MPS technologies and highlight future directions for their integration with AI.