Foundations and Neural Infrastructure: Translating AI into Clinical Neuroscience
"All those amazing models could be helping patients … if they’re never used, they’ll never help anyone." Dr. Santiago Romero Brufau, Harvard T.H. Chan School of Public Health
If the algorithms are so sophisticated, why do they often fail to reach patients?
Artificial intelligence (AI) in neuroscience is often portrayed as a revolution, but the reality is more tempered. Many of you have followed this journey from LinkedIn, drawn by reflections on AI, neuroscience, and ethics. As a NeuroEdge Nexus LINK, we encourages deeper exploration of the emerging interface of AI and neurology, clinical neurophysiology and neuroscience focusing on analysis, interpretation, and critical thinking rather than prescriptive instruction.
The imperative is clear: AI in neuroscience is not merely academic. Its promise can only be realized when supported by robust computational infrastructure, interoperable platforms, regulatory frameworks, and ethically grounded oversight. Without these scaffolds, even the most sophisticated algorithms remain theoretical — insightful yet unable to impact patient outcomes.
This season, “Foundations & Neural Infrastructure "LINK, will explore how computational, regulatory, and ethical frameworks are indispensable for translating AI into meaningful clinical outcomes. Our year-long journey alternates between foundational essays (data, governance, infrastructure, ethics) and practical applications in neurology, clinical neurophysiology, and neuromodulation. Only by understanding both sides of these layers can one responsibly deploy AI in practice.
Healthcare professionals face a challenge: much of neuroscience research remains locked in dense academic literature, while popularized content oversimplifies mechanisms, offering little actionable insight. At the same time, AI tools and neuromodulation technologies are evolving rapidly, outpacing conventional communication channels.