Reply to “ <scp>AI:</scp> Need for Comparative Studies in Complex Neurologic Diagnosis”

As Dr. Finelli notes, diagnostic generators and expert clinicians often outperform artificial intelligence (AI) in complex scenarios, and current AI methods may not capture the nuanced reasoning that underlies neurologic practice. Comparative, externally validated studies are, therefore, essential to assure safety, reliability, and equity before AI achieves widespread use. Adherence to V3 principles—verification, validation, and vigilance—will be crucial to safeguarding accuracy and sustaining clinical trust. The United States Food and Drug Administration has likewise advised that good machine learning practice1 and evidence frameworks are important when evaluating AI/machine learning-based medical devices, emphasizing iterative validation and post-market vigilance as requirements for safe integration. At the same time, neurologists should remain attentive to emerging AI applications already reshaping care: early detection through imaging and electroencephalography, digital biomarkers from sensors and wearables, personalized prognostic models, decision support in stroke and epilepsy, acceleration of drug discovery, and more.2 These themes are explored further in Part 2: Clinical Applications3 and extended in Part 3: Surveillance, Synthesis, Simulation, and Systems,4 which highlight both opportunities and system-level challenges for safe deployment. Explainable AI (XAI) will be critical in this transition, offering transparency into model reasoning and enabling clinicians to discern when outputs can be trusted. Practical adoption also requires vigilance. Clinicians must verify outputs against authoritative sources, seek corroborating evidence, and maintain human oversight. Continuing education will be essential, as no individual can master all of AI's expanding landscape; instead, neurologists should cultivate proficiencies— native, fluent, or conversational—in the domains most pertinent to their daily practice. We appreciate Dr. Finelli's contribution and agree that comparative evidence, transparency, safeguards, and lifelong learning will be central to advancing AI responsibly in neurology. Matthew Rizzo: Conceptualization; writing – original draft; writing – review and editing; data curation. Jeffrey D. Dawson: Writing – review and editing; data curation.