August 18, 2026

@michaelokun

AI in neurology has a translation problem: can we move from cool algorithms to better care? Artificial intelligence (AI) refers to computer systems that can learn patterns from data and use those patterns to make predictions, classifications or recommendations. James Teo and colleagues describe in a new Perspective in Nature Reviews Neurology why AI has reached an inflection point in neurology, but despite impressive research and numerous FDA authorized algorithms, real-world clinical impact remains limited. Key points: - AI tools frequently perform impressively in research settings, but many stall when moving through validation, regulation and implementation into real clinical workflows. - Neurology has major challenges including fragmented data, inadequate interoperability, bias, data drift and too little prospective multi-site validation. - The authors argue we should shift resources from simply creating more algorithms toward implementation science, pragmatic clinical trials, infrastructure and long-term partnerships that can safely scale successful tools. My take: We have spent a lot of time asking whether AI can work in neurology. I agree w/ the authors that it is time to ask a harder question: can AI actually improve care at scale? An algorithm that performs beautifully on a research dataset but never reaches the bedside does not help our persons w/ disease. I was particularly struck by the observation that AI has advanced more rapidly in fields like radiology and cardiology where data infrastructure and standards are more mature, while areas like AD, PD and movement disorders have progressed more slowly. The next breakthrough may therefore be less about inventing another clever algorithm and more about building the infrastructure, validation, workflow and trust needed to put the best algorithms to work. Here are a couple of points that resonated w/ me: 1- AI should solve important clinical problems rather than simply demonstrate that another algorithm can outperform a benchmark. 2- Validation matters. An AI model trained at one institution may fail when exposed to different folks, equipment, workflows or changing clinical practice.