Building Trustworthy and Equitable Artificial Intelligence for Neurology

The intersection of artificial intelligence and neurology has produced significant advancements in disease detection and clinical workflow optimization. Despite these gains, a critical and underexplored challenge lies in ensuring that AI systems used for neurological disorders are both ethically robust and practically trustworthy in real-world healthcare environments. Particularly in neurology, the complexity, dimensionality, and heterogeneity of neuroimaging and electrophysiology datasets compound risks related to patient privacy, model transparency, and equitable access to innovation. Recent studies have explored AI’s diagnostic potential for specific diseases or imaging modalities, yet few have addressed how to operationalize privacy, security, and fairness while managing large-scale, sensitive neurological data. Current approaches often underperform when required to generalize across diverse patient populations and care settings, leaving a gap in translating technical capabilities into reliable clinical practice.

This Research Topic aims to advance the field of neurology by fostering research into the foundational frameworks and practices that underpin trustworthy, secure, and ethical AI adoption. Rather than focusing solely on predictive accuracy or clinical applications, we seek submissions that explore new paradigms for maintaining privacy, fairness, and explainability throughout the AI lifecycle in neurology. Core questions we hope to address include: How can privacy-preserving techniques be optimized for high-dimensional, multi-modal neuro-data without undermining diagnostic utility? What are best practices for building generalizable and interpretable AI models that clinicians can trust? How do we ensure fairness and inclusivity when deploying AI systems across varied patient demographics and healthcare infrastructures? The ultimate goal is to mobilize the community toward AI solutions that are not just innovative, but also clinically credible, ethically compliant, and broadly applicable.

This Research Topic welcomes contributions that emphasize methodological advances, scalable frameworks, and policy-oriented discussions relevant to trustworthy AI in neurology. Work submitted should proactively confront the real-world barriers to AI adoption, with an explicit focus on privacy, security, transparency, and equity in neurological data science. We invite articles including, but not limited to:

o Privacy-preserving and federated learning tailored to neuro-data

o Explainable AI frameworks designed for complex neuroimaging and electrophysiological datasets

o Strategies for bias mitigation and algorithmic fairness in neurological AI

o Secure integration of AI into clinical neurology workflows and digital diagnostics

o Evaluation of policy, legal, and ethical considerations relevant to neurological AI deployment

o Standardization and benchmarking practices for robust, reproducible AI in real-world neurology settings

We encourage Original Research, Reviews, Methods, and Policy Discussions that advance these themes and contribute to a more equitable, secure, and accountable future for AI in neurology.