The next major frontier is likely precision enhancement: matching interventions to baseline phenotype, genotype, sleep state, age, microbiome profile, and task demands. Rather than asking whether a compound “works,” researchers increasingly ask for whom, under what conditions, and via what mechanisms it works. This pushes the field toward personalized models and adaptive trials.

Wearables, digital phenotyping, and real-time physiological monitoring enable adaptive dosing and closed-loop intervention paradigms. These methods may help optimize timing and minimize harm, but they also raise major concerns about noise, overfitting, privacy, and algorithmic opacity. A robust future science of enhancement will need to integrate pharmacology with computation and behavioral context.

Several core problems remain unresolved: how to measure true enhancement beyond practice effects; how to predict responders; how to model long-term safety in healthy populations; how to compare pharmacological and non-pharmacological interventions on equal footing; and how to incorporate cognition, mood, and function into unified endpoints. The literature remains especially thin on long-duration, real-world, and multi-modal designs.