Precision music medicine hinges on integrating musical expertise into study design, defining interventions by musical features rather than genres, and using AI to solve the matching problem between patient physiology and musical intervention.

Researchers measure biological responses to music with multimodal biomarkers—including HRV, inflammatory markers such as IL-6 and CRP, cortisol, EEG, fMRI, proteomics, metabolomics, pupillometry, and wearables—going beyond self-reported outcomes.

Music-STAR is a three-arm randomized trial in neurosurgical patients comparing simulated live music, recorded music, and no music, designed to yield prospective evidence.

Awe-inducing, large-scale music could offer distinct clinical benefits beyond relaxation, potentially reducing inflammation and shifting self-focus in seriously ill patients.

A real-time, closed-loop system is a key goal, requiring feedback during performance to influence music as it unfolds rather than only after the fact.

Current evidence suggests music interventions can reduce anxiety, pain, and fatigue across settings, but heterogeneity in interventions presents a challenge.

The program differentiates music medicine from music therapy, focusing on prerecorded or live music as a scalable clinical intervention, with therapists providing a distinct, complementary service.

The long-term vision envisions wearable-enabled, biomarker-guided music prescriptions that adapt in real time, turning music into a precisely controlled medical intervention rather than entertainment.

Dr. Mei Rui, a concert pianist and MD Anderson neurosurgery associate professor, leads Music-in-Medicine at MD Anderson, pursuing precision music medicine through biomarker-driven trials.

To enable reproducibility, the field must specify musical interventions using precise parameters—timbre, tempo, dynamics, phrasing—rather than treating music as a generic stimulus.

A brain-computer interface with real-time EEG is used in hyperscanning concerts to visualize neural changes with musical variations, alongside measurements of HR, HRV, breathing, skin conductance, and pupillometry.

Computational tools (MIRtoolbox, Librosa, Essentia) extract musical features such as tempo, harmony, dynamics, and rhythmic complexity to link music properties with physiological responses.