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<p>Artificial intelligence (AI) has emerged as a transformative force in neurology, offering unprecedented potential to enhance diagnostic precision, streamline clinical workflows, and accelerate translational research. However, the integration of AI into routine neurology practice is accompanied by substantial challenges, including inherent biases in training datasets, regulatory ambiguities, and risks associated with over-reliance on algorithmic outputs, which also expose critical gaps in neurology education and research infrastructure. This review synthesizes the current state of AI applications in neurology with a focus on stroke detection and electroencephalogram (EEG) analysis for epilepsy and examines critical pitfalls exemplified by IBM Watson’s underperformance in neuro-oncology. Our methodology involved a targeted literature search of studies published between January 2018 and December 2024, prioritizing large multicenter validations, reports on demographic diversity, and implementations in non-Western settings. We further outline actionable recommendations to mitigate these risks, emphasizing the need for multicultural training datasets, standardized regulatory frameworks, structured human–AI collaboration models centered on “AI steward” roles, and strengthened AI education and research infrastructure. By addressing these contemporary issues in practice, education, and research, neurology can harness the full promise of AI while safeguarding patient care equity and quality.</p>

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Artificial intelligence in neurology practice: promise, perils, and a roadmap for responsible integration

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Xingli Zhou,
  • Seidu A. Richard,
  • Zhigang Lan,
  • Sharma Madhusudan,
  • Rui Zhang

摘要

Artificial intelligence (AI) has emerged as a transformative force in neurology, offering unprecedented potential to enhance diagnostic precision, streamline clinical workflows, and accelerate translational research. However, the integration of AI into routine neurology practice is accompanied by substantial challenges, including inherent biases in training datasets, regulatory ambiguities, and risks associated with over-reliance on algorithmic outputs, which also expose critical gaps in neurology education and research infrastructure. This review synthesizes the current state of AI applications in neurology with a focus on stroke detection and electroencephalogram (EEG) analysis for epilepsy and examines critical pitfalls exemplified by IBM Watson’s underperformance in neuro-oncology. Our methodology involved a targeted literature search of studies published between January 2018 and December 2024, prioritizing large multicenter validations, reports on demographic diversity, and implementations in non-Western settings. We further outline actionable recommendations to mitigate these risks, emphasizing the need for multicultural training datasets, standardized regulatory frameworks, structured human–AI collaboration models centered on “AI steward” roles, and strengthened AI education and research infrastructure. By addressing these contemporary issues in practice, education, and research, neurology can harness the full promise of AI while safeguarding patient care equity and quality.