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Artificial intelligence in neurology: opportunities, challenges, and policy implications
Sebastian Voigtlaender1,2, Johannes Pawelczyk3,4, Mario Geiger5,6
1Systems Neuroscience Division, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
Journal of Neurology
|February 17, 2024
Summary
Artificial intelligence (AI) offers transformative potential for neurological research and brain health, addressing the leading causes of disability. Addressing challenges in AI models, data, equity, and regulation is crucial for its effective clinical integration.
Area of Science:
- Neurology and Neuroscience
- Artificial Intelligence in Healthcare
- Global Brain Health Initiatives
Background:
- Neurological conditions represent a significant global burden, driving the need for advanced solutions.
- Brain health is a recognized global priority, underscored by the World Health Organization's 2022 action plan.
- Rapid advancements in artificial intelligence (AI) are poised to reshape neurological research and clinical practice.
Purpose of the Study:
- To conduct a scoping review of 66 original articles to explore the value of AI in neurology and brain health.
- To systematize the landscape of AI applications across the neurological care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation.
- To identify emergent clinical opportunities and future trends for AI in brain health.
Main Methods:
- Systematic scoping review methodology.
- Analysis of 66 original research articles focusing on AI in neurology and brain health.
- Categorization of AI applications across the full spectrum of neurological patient care.
Main Results:
- AI demonstrates significant potential across all stages of neurological care, from prevention to rehabilitation.
- Key challenges hindering AI integration include issues related to AI models, data availability and quality, feasibility, equity, and regulatory frameworks.
- Personalized precision neurology and global brain health initiatives can be advanced through strategic AI implementation.
Conclusions:
- Concerted efforts are needed to address the identified challenges across four pillars: models, data, feasibility/equity, and regulation/innovation.
- Paramount actions include ethical, equity-focused integration of AI technologies into clinical workflows.
- Mitigating data-related issues, bridging digital inequity gaps, and establishing robust governance are essential for balancing safety and innovation in AI for neurology.
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