From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital...

Brain disorders have traditionally been managed through fragmented clinical pathways in which diagnosis, monitoring, intervention, and long-term follow-up are treated as separate processes. This paradigm is increasingly insufficient for conditions such as Alzheimer's disease, Parkinson's disease, stroke, traumatic brain injury, glioma, epilepsy, and neuropsychiatric disorders, whose trajectories are dynamic, heterogeneous, and strongly shaped by biological, behavioral, environmental, and health-system factors. Recent advances in artificial intelligence, digital health, digital twin modeling, and advanced biomaterials provide an opportunity to move beyond isolated technological applications toward integrated brain health systems. In this Perspective, we argue that the major translational opportunity is not simply to apply artificial intelligence to neuroimaging, use wearable devices for neurological monitoring, or develop biomaterials for brain repair in parallel. Rather, the field should aim to build closed-loop systems in which computational models identify disease states, digital platforms continuously update patient trajectories, and advanced materials deliver adaptive therapeutic or regenerative interventions. We propose that future progress depends on three related shifts: moving from episodic diagnosis to longitudinal brain-state modeling; redefining advanced materials as programmable therapeutic interfaces rather than passive carriers; and evaluating success at the level of health-system integration rather than single-device performance. This framework may help reorient brain disease innovation from fragmented neurotechnologies toward clinically deployable, patient-specific, and dynamically adaptive brain health systems.