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<p>Artificial intelligence (AI) applications in neurology have reached an inflection point. Despite US Food and Drug Administration approval of numerous algorithms in neuroimaging, neurophysiology, genetics and chatbots, their real-world impact remains limited. This disconnect between research promise and clinical reality represents a gap in understanding how to translate AI algorithms into clinical benefit for patients globally. In this Perspective, we examine the challenges that prevent clinical AI use in neurology moving beyond pilot studies towards meaningful clinical impact. We consider the steps required in the process of translation, including research, validation of AI models, regulatory approval pathways and clinical implementation. We discuss implementation of AI models as stand-alone products versus embedded platforms, and the requirements for sustainable deployment. Beyond traditional clinical decision support tools, we examine paraclinical applications of AI, including chatbots and ambient voice documentation. We recommend expanding capacity for prospective validation and scaling by implementing and validating technologies across multiple sites and countries, which requires infrastructure from long-term partnerships. Neurology must shift from asking whether AI can work to understanding how to use it safely at scale.</p>

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Moving artificial intelligence from research to real-world clinical use in neurology

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • James T. Teo,
  • Arman Eshaghi,
  • Mark P. Richardson,
  • Lara Jehi,
  • Sandor Beniczky

摘要

Artificial intelligence (AI) applications in neurology have reached an inflection point. Despite US Food and Drug Administration approval of numerous algorithms in neuroimaging, neurophysiology, genetics and chatbots, their real-world impact remains limited. This disconnect between research promise and clinical reality represents a gap in understanding how to translate AI algorithms into clinical benefit for patients globally. In this Perspective, we examine the challenges that prevent clinical AI use in neurology moving beyond pilot studies towards meaningful clinical impact. We consider the steps required in the process of translation, including research, validation of AI models, regulatory approval pathways and clinical implementation. We discuss implementation of AI models as stand-alone products versus embedded platforms, and the requirements for sustainable deployment. Beyond traditional clinical decision support tools, we examine paraclinical applications of AI, including chatbots and ambient voice documentation. We recommend expanding capacity for prospective validation and scaling by implementing and validating technologies across multiple sites and countries, which requires infrastructure from long-term partnerships. Neurology must shift from asking whether AI can work to understanding how to use it safely at scale.