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<p>Validation is a cornerstone of reliability and trust in diagnostics, yet discipline-specific assumptions and unspoken contextual differences often lead to miscommunication, misalignment, and avoidable delays. As AI/ML becomes more integrated into healthcare, there is a growing necessity to re-examine how the term <i>validation</i> is used and understood. We highlight inconsistencies in the use of the term <i>validation</i> through an analysis of 94 themes across five domains, including Communication Science (<i>n</i> = 12), AI/ML (<i>n</i> = 26), Clinical and Laboratory Practice (<i>n</i> = 19), Regulatory Science (<i>n</i> = 22), and Business (<i>n</i> = 15). We emphasize how persistent reliance on domain-specific implied definitions impedes interdisciplinary alignment. Rather than advocating for a single definition, we derived five consensus proposals that collectively advocate for more specific and context-aware additions to the term <i>validation</i> to support clarity, reliability, and compliance across disciplines. Our goal is to support clearer communication and provide useful strategies that inform the development, regulation, and use of digital health technologies.</p>

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Clarifying validation terminologies in healthcare

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Amanda Dy,
  • Sandra M. Buetow,
  • Andrew J. Bredemeyer,
  • Monika Lamba Saini,
  • Fabienne Lucas,
  • Shannon Bennett,
  • Kim R. M. Blenman,
  • Keith Wharton Jr.,
  • Sunil Singhal,
  • M. E. de Baca,
  • Kevin Schap,
  • Matthew G. Hanna,
  • Staci J. Kearney,
  • Norman Zerbe,
  • Roberto Salgado,
  • Jithesh Veetil,
  • Jansen N. Seheult,
  • David S. McClintock,
  • April Khademi,
  • Jochen K. Lennerz

摘要

Validation is a cornerstone of reliability and trust in diagnostics, yet discipline-specific assumptions and unspoken contextual differences often lead to miscommunication, misalignment, and avoidable delays. As AI/ML becomes more integrated into healthcare, there is a growing necessity to re-examine how the term validation is used and understood. We highlight inconsistencies in the use of the term validation through an analysis of 94 themes across five domains, including Communication Science (n = 12), AI/ML (n = 26), Clinical and Laboratory Practice (n = 19), Regulatory Science (n = 22), and Business (n = 15). We emphasize how persistent reliance on domain-specific implied definitions impedes interdisciplinary alignment. Rather than advocating for a single definition, we derived five consensus proposals that collectively advocate for more specific and context-aware additions to the term validation to support clarity, reliability, and compliance across disciplines. Our goal is to support clearer communication and provide useful strategies that inform the development, regulation, and use of digital health technologies.