AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.

Area of Science:

• Neuroscience
• Artificial Intelligence
• Medical Informatics

Background:

• Artificial intelligence (AI) is increasingly impacting healthcare, but its complexities remain a barrier for many professionals and patients.
• Understanding AI's fundamental principles is crucial for its effective integration into clinical practice and research.

Purpose of the Study:

• To provide neurologists and neuroscientists with a foundational understanding of AI concepts, terminology, and applications.
• To contextualize AI development by tracing its origins in mathematics, logic, and neural networks.
• To highlight AI's transformative potential in neurological diagnostics, treatment, and precision care.

Main Methods:

• Review of AI's historical development, mathematical underpinnings, and brain-inspired neural network models.
• Analysis of machine learning applications relevant to neurology, including computer vision and brain-machine interfaces.
• Exploration of AI model selection principles and a case scenario for precision neurological care.

Main Results:

• AI's evolution is intrinsically linked to neuroscience and human reasoning, demonstrating its capacity to reshape neurological practice.
• Machine learning techniques are pivotal in advancing neurological diagnostics, treatment planning, and personalized patient care.
• The review establishes a framework for understanding AI's current and future impact on neurology.

Conclusions:

• This foundational review equips neurologists and neuroscientists with essential AI knowledge for navigating its application in neurological fields.
• AI is poised to drive significant advancements in precision neurological care and research.
• This work serves as the first part of a series, setting the stage for deeper dives into AI's practical integration and broader societal impact.