Twenty-Five Years of AI in Neurology: The Journey of Predictive Medicine and the Emergence of Neuroinformatics (Preprint)

Abstract

UNSTRUCTURED Neurological disorders are the leading cause of physical and cognitive disability across the globe, currently affecting up to 15% of the world population, with burden of chronic neurodegenerative diseases doubled over the last two decades. Two decades ago, neurologists relying solely on clinical signs and basic imaging faced challenges in diagnosis and treatment. Today, the integration of artificial intelligence and bioinformatic methods are changing this landscape. This review explores this transformative journey, emphasizing the critical role of bioinformatics in neurology, which we term as Neuroinformatics, aiming to integrate a multitude of methods and thereby enhance the field of neurology. Over the past 25 years, the integration of biomedical data science into medicine, particularly neurology, has fundamentally transformed how we understand, diagnose, and treat neurological diseases. Advances in genomics sequencing, the introduction of new imaging methods, the discovery of novel molecular biomarkers for nervous system function, a comprehensive understanding of immunology and neuroimmunology shaping disease subtypes, and the advent of advanced electrophysiological recording methods, alongside the digitalization of medical records and the rise of AI, all led to an unparalleled surge in data within neurology. Additionally, telemedicine and virtual health platforms, accelerated by the COVID-19 pandemic, have become integral to neurology practice. The real-world impact of these advancements is evident, with AI-driven analysis of imaging and genetic data leading to earlier and more accurate diagnoses of conditions like Multiple Sclerosis, Parkinson's Disease, Amyotrophic Lateral Sclerosis, Alzheimer’s Disease and more. Neuroinformatics is the key component connecting all these advances. By harnessing the power of information technology and computational methods to efficiently organize, analyze, and interpret vast datasets, we can extract meaningful insights from complex neurological data, contributing to a deeper understanding of the intricate workings of the brain. In this review, we describe the large-scale datasets that have emerged in neurology over the last 25 years and showcase the major advancements made by integrating these datasets with advanced neuroinformatic approaches for the diagnosis and treatment of neurological disorders. We further discuss challenges in integrating AI into neurology, including ethical considerations in data use, the need for further personalization of treatment, and embracing new emerging technologies like quantum computing. These developments are shaping a future where neurological care is more precise, accessible, and tailored to individual patient needs. We believe further advancements in neuroinformatics will bridge traditional medical disciplines and cutting-edge technology, navigating the complexities of neurological data and steering medicine toward a future of more precise, accessible, and patient-centric healthcare.