Artificial Intelligence as the Computational Framework for Adaptive Closed-Loop Neuromodulation in Psychiatry

Abstract

Adaptive neuromodulation offers a path toward personalized neuropsychiatric treatment by allowing therapy to respond to clinically meaningful neurophysiological and behavioral changes. Its clearest clinical precedents are responsive neurostimulation for epilepsy and adaptive deep brain stimulation for movement disorders. Both rely on signals that can be measured reliably and linked directly to stimulation. Psychiatric disorders pose a different problem: symptoms are heterogeneous, evolve over multiple timescales, and are observed only indirectly through noisy and incomplete measurements. Artificial intelligence can provide data-driven components of an adaptive neuromodulation system, connecting multimodal sensing, inference of patient state, prediction of treatment response, and constrained therapeutic adaptation. Interpretability is required throughout to support clinician and patient trust. Clinical evidence, however, remains limited to small proof-of-concept studies. Translation to practice will require replicated biomarkers and prospective multisite validation. It will also require explicit management of uncertainty and system designs that match adaptation to symptom and treatment timescales. Clinician and patient oversight must be preserved throughout, alongside safety constraints and governance across the treatment life cycle. This review presents a system-design view of how these components combine into a single adaptive architecture for psychiatric neuromodulation.