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<p>Clinical documentation accounts for a substantial share of clinicians’ working time, contributing to administrative burden and reduced patient-facing care. Artificial intelligence has stimulated the development of digital scribes that combine speech recognition (ASR) and large language models (LLMs) to generate clinical notes from patient–provider conversations with the aim to automate and support this process and reduce this burden. This scoping review explores how digital scribes are currently validated, both technical and clinical, and whether they reliably support clinical workflows. Using the Technology Readiness Level (TRL) framework, we show that most systems remain in early development stages (typically TRL 3&amp;4), with only a small number progressing to workflow integration. While digital scribes show potential to improve documentation efficiency, validation methods are highly heterogeneous, most studies rely on simulated or retrospective data, and real-world testing is limited. Consequently, cross-system comparisons and conclusions about clinical performance remain limited. We identified three motivational frames: human-, performance-, and system-oriented, which shape evaluation practices and outcome expectations. These findings suggest that successful implementation depends not only on scribes’ technical capability but also on alignment with clinical needs and documentation styles. Overall, our review underscores the need for standardised validation frameworks and prospective real-world studies to ensure that digital scribes progress beyond their current low TRL and move from experimental promise to safe, effective, and sustainable integration into clinical care.</p>

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Validating Digital Scribes: A Scoping Review of Evaluation Practices and Clinical Use

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月24日

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  • Ekin Kerimoğlu,
  • Fleur V. Notermans,
  • Milou E. W. M. Silkens,
  • Marleen de Mul,
  • Kees C. T. B. Ahaus,
  • Robert M. A. van der Boon

摘要

Clinical documentation accounts for a substantial share of clinicians’ working time, contributing to administrative burden and reduced patient-facing care. Artificial intelligence has stimulated the development of digital scribes that combine speech recognition (ASR) and large language models (LLMs) to generate clinical notes from patient–provider conversations with the aim to automate and support this process and reduce this burden. This scoping review explores how digital scribes are currently validated, both technical and clinical, and whether they reliably support clinical workflows. Using the Technology Readiness Level (TRL) framework, we show that most systems remain in early development stages (typically TRL 3&4), with only a small number progressing to workflow integration. While digital scribes show potential to improve documentation efficiency, validation methods are highly heterogeneous, most studies rely on simulated or retrospective data, and real-world testing is limited. Consequently, cross-system comparisons and conclusions about clinical performance remain limited. We identified three motivational frames: human-, performance-, and system-oriented, which shape evaluation practices and outcome expectations. These findings suggest that successful implementation depends not only on scribes’ technical capability but also on alignment with clinical needs and documentation styles. Overall, our review underscores the need for standardised validation frameworks and prospective real-world studies to ensure that digital scribes progress beyond their current low TRL and move from experimental promise to safe, effective, and sustainable integration into clinical care.