Mental Illness · Published 2026-01-01 · DOI 10.1155/mij/7902487
Music-based interventions may offer accessible adjunctive support for several mental conditions, but average treatment effects are heterogeneous and objective outcomes are often inconsistent. Artificial intelligence (AI) can select or generate music at scale, yet tailoring the musical stimulus alone does not establish a biologically individualized treatment. This conceptual perspective distinguishes three levels of individualization: the patient's clinical and cultural context, the neural or psychophysiological target, and the musical stimulus and dose. Group-level disorder-network findings should serve only as provisional priors. Before an AI system adapts music to presumed target, the candidate target should demonstrate within-person reliability, relevance to the intended symptom or function, directional interpretability, and modifiability by music relative to an active control. We refine two development pathways: theory-driven AI selection and data-driven AI composition. Both require patient-specific calibration, prespecified falsification criteria, held-out and external validation, subgroup fairness analyses, clinician oversight, and confirmation that changes in a neural surrogate correspond to clinically meaningful outcomes. Electroencephalography is currently the most feasible primary signal for a real-time feedback loop; peripheral physiology and functional near-infrared spectroscopy may provide complementary information, whereas functional magnetic resonance imaging is more practical for offline target discovery and calibration. Individualized music therapy should not be defined as AI-generated music optimized toward a group-average disorder signature. A defensible model must individualize both the therapeutic target and the musical intervention, place patient goals and culture within the optimization process, and remain falsifiable, equitable, and clinician supervised.
Abstract from DOAJ. Public domain (CC0 1.0).
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