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

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3-part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications. We begin by tracing AI's origins in mathematics, human logic, and brain-inspired neural networks to establish a context for its development. The review highlights AI's growing role in neurological diagnostics and treatment, emphasizing machine learning applications, such as computer vision, brain-machine interfaces, and precision care. By mapping the evolution of AI tools and linking them to neuroscience and human reasoning, we illustrate how AI is reshaping neurological practice and research. We end the review with an overview of model selection in AI and a case scenario illustrating how AI may drive precision neurological care. Part 1 sets the stage for part 2, which will focus on practical applications of AI in real-world scenarios where humans and AI collaborate as joint cognitive systems. Part 3 will examine AI's integration with extensive healthcare and neurology networks, innovative clinical trials, and massive datasets, expanding our vision of AI's global impact on neurology, healthcare systems, and society. ANN NEUROL 2025;98:211-230.

FIGURE 1

Hierarchical relationships between logic, artificial intelligence (AI), machine learning (ML), and deep learning (DL) within sociotechnical systems for healthcare. Logic forms the foundation for reasoning and systematic thinking. AI leverages logical structures for intelligent machine behavior. ML involves algorithms that generate models, which improve through training. DL, a specialized ML area, focuses on multi‐layered neural networks, including convolutional neural networks, generative adversarial networks, and large language models. Robotics relies on AI for perception, planning, and decision‐making, and on ML for adaptive functions. Robotics also involves mechanical engineering, sensors, and control systems beyond AI's scope. Standard statistics, fundamental to neuroscience and other fields, fit primarily within the “Logic” oval, extends into “AI,” and is deeply embedded in ML for training and evaluation. Sociotechnical systems represent the broadest context, encompassing human, organizational, and technological elements bound by AI and operated by humans. [Color figure can be viewed at www.annalsofneurology.org]

FIGURE 2

Workflow diagram combining classical biostatistical techniques with artificial intelligence (AI)/machine learning (ML) in neurology. Modern workflow that combines biostatistical methods with AI and ML to analyze real‐world data. This approach examines the impact of neurological conditions on real‐world functioning, capturing the “brain in the wild,” and predicting diseases using novel phenotypes from digital biomarkers. The process starts with collecting data from sources like patient demographics, medical assessments, functional abilities, and real‐world behavioral and physiological data, which is then organized, cleaned, and synchronized for analysis. Multivariate and ML techniques are used, incorporating computer vision, statistical analysis, geographic information systems (GIS), human review, and pattern categorization. Large datasets are processed efficiently using high‐performance computing and graphics processing unit (GPU) clusters, with quality assurance to ensure accuracy and reliability. Applications include medical diagnostics, disease progression prediction, treatment efficacy assessment, safety evaluations, social interaction analysis, lifespace assessments, and quality‐of‐life measurements. By integrating statistical analysis, design methodologies, and AI/ML, this approach enhances the understanding of neurological disorders and improves patient outcomes. MCI, mild cognitive impairment; AD, Alzheimer's disease; NIA‐AA, National Institute on Aging and Alzheimer's Association. [Color figure can be viewed at www.annalsofneurology.org]