Digital Twins in Neurology Care: Evidence, Limitations, and a Path Forward

Digital twins are increasingly promoted in neurology as an advancement beyond conventional artificial intelligence, yet the term is often applied without conceptual or methodological rigor. Strict definitions describe digital twins as dynamically updated, bidirectionally linked models that generate predictive, decision-relevant value, criteria rarely met by current neurological applications. This Opinion critically examines the state of digital twins across major neurological domains, including dementia, multiple sclerosis, Parkinson's disease, epilepsy, stroke, pain, and migraine. We argue that most existing systems are more accurately described as twin-inspired longitudinal decision-support or trial-analytics models rather than true clinical digital twins. While neurology is well-suited to digital twin approaches due to disease heterogeneity, multimodal data, and iterative care pathways, progress is limited by gaps in measurement validity, uncertainty handling, prospective evaluation, and governance. A pragmatic path forward is proposed, emphasizing question-specific, validated neurological digital twins over overgeneralized brain twin narratives, and suggesting that much of the current field is better understood as twin-inspired modeling rather than true clinical digital twin implementation.