Artificial intelligence is reshaping many fields, and its impact on healthcare grows by the day. Computational neuroscience, using mathematical and computational tools to understand brain function, is perfectly placed to benefit. Today’s datasets are vast and varied, spanning EEG and MEG recordings, fMRI and other imaging, as well as genetic and behavioral measures, alongside computational vision and human–computer interaction (HCI) signals (e.g., eye-tracking) that capture sensory and perceptual dynamics. AI methods excel at making sense of this high-dimensional information, revealing subtle patterns linked to neurological and psychiatric conditions. Bringing these tools into clinical neuroscience can improve diagnosis, forecast disease trajectories, and tailor treatments to individual patients, helping close the gap between basic brain research and real-world care.

AI is advancing quickly in computational neuroscience, but we need a clear-eyed view of its real clinical value. Many studies show promise, using AI to interpret neuroimaging, electrophysiology, and behavioral data for diagnosis and treatment, yet moving from proof-of-concept to tools that work reliably at the bedside is hard. Challenges include inconsistent data standards, opaque models, limited generalization across diverse populations, regulatory hurdles, and ethical risks. This Research Topic responds by curating high-quality reviews that take stock of the field: what’s working, where the limitations and biases lie, and what still block translation into routine care. We also welcome perspectives on hybrid AI frameworks that combine probabilistic structure with data-driven learning to model temporal dependencies and uncertainty in neural and behavioral data. Our aim is to consolidate the evidence, highlight best practices, and outline a practical agenda for AI that is robust, transparent, and genuinely useful in clinical settings.

This Research Topic invites rigorous review articles that critically assess the role of artificial intelligence in clinical computational neuroscience. We welcome submissions that synthesize the current literature, spotlight major advances, and candidly discuss outstanding challenges and future directions for translating AI-derived insights into routine care. Contributions should provide a balanced view of both the promise and the limitations of these technologies, including behavioral/HCI approaches, particularly within computational vision, that broaden techniques for clinical and sensory-driven behavioral assessment.

Suggested themes include (but are not limited to):

- AI for diagnosis, prognosis, and subtyping/stratification in neurological and psychiatric disorders

- Personalized therapeutic planning and treatment-response prediction using AI

- Model interpretability and explainable AI (XAI) in clinical workflows

- Challenges in multimodal data integration, generalization across sites/populations, and external validation/benchmarking

- Ethical, regulatory, and societal considerations (bias, equity, privacy, safety) in clinical AI

- AI-driven computational models for mechanistic insight and biomarker discovery

- Focused reviews of specific AI paradigms (e.g., deep learning, graph neural networks) applied to neuroimaging (MRI, fMRI, PET) and electrophysiology (EEG/MEG)

- Behavioral and HCI approaches in computational vision for clinical assessment and monitoring

- Hybrid AI frameworks for integrating neural and behavioral data and modeling temporal dependencies

Are you interested in contributing a manuscript to this Research Topic?

Reviews in: AI for clinical applications in computational neuroscience