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<p>Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful AI adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness its potential for regulatory responsibilities in the evolving 21st-century landscape.</p>

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Is regulatory science ready for artificial intelligence?

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月10日

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  • Thomas Hartung,
  • Maurice Whelan,
  • Weida Tong,
  • Robert M. Califf

摘要

Trust is key in AI for regulatory science, but its definition is debated. If AI models use different features yet perform similarly, which should be trusted? If scientific theories must be testable, how critical is explainability? At the Global Summit on Regulatory Science (GSRS24), regulators agreed that successful AI adoption requires ongoing dialogue, adaptability, and AI-trained personnel to harness its potential for regulatory responsibilities in the evolving 21st-century landscape.