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<p>Artificial intelligence (AI) holds promise for healthcare, but real-world implementation remains difficult. The Mayo clinic platform (MCP) addresses this by providing scalable, multi-institutional, de-identified data and analytical tools. Through four research projects, we demonstrate MCP’s ability to support efficient cohort identification, AI model development, and real-world evidence generation. MCP enables broader accessibility and standardization compared to institutional EHRs, positioning it as a powerful platform for advancing translational research and precision medicine.</p>

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Accelerating AI innovation in healthcare: real-world clinical research applications on the Mayo Clinic Platform

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月16日

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  • Yue Yu,
  • Xinyue Hu,
  • Sivaraman Rajaganapathy,
  • Jingna Feng,
  • Ahmed Abdelhameed,
  • Xiaodi Li,
  • Jianfu Li,
  • Xiaoke Liu,
  • Liu Yang,
  • Nilüfer Ertekin-Taner,
  • Phil Fiero,
  • Soulmaz Boroumand,
  • Richard Larsen,
  • Maneesh Goyal,
  • Clark C. Otley,
  • Nansu Zong,
  • Vijay H. Shah,
  • John D. Halamka,
  • Cui Tao

摘要

Artificial intelligence (AI) holds promise for healthcare, but real-world implementation remains difficult. The Mayo clinic platform (MCP) addresses this by providing scalable, multi-institutional, de-identified data and analytical tools. Through four research projects, we demonstrate MCP’s ability to support efficient cohort identification, AI model development, and real-world evidence generation. MCP enables broader accessibility and standardization compared to institutional EHRs, positioning it as a powerful platform for advancing translational research and precision medicine.