AI models for medication adherence prediction: closing the gap to clinical readiness

Artificial intelligence (AI) could help identify patients at risk of medication non-adherence, but the clinical readiness of published prediction models is uncertain. We systematically reviewed 41 adherence prediction modelling studies and assessed model development and evaluation using PROBAST + AI across participants/data sources, predictors, outcomes, and analyses. Most models showed great concern for development quality (71%) and high risk of bias in evaluation (80%), commonly due to poorly defined adherence outcomes, inadequate handling of missing data, and limited validation. Reported discrimination did not consistently improve with more complex algorithms, indicating that methodological rigour, rather than model type, is the key barrier to translation. We provide framework-guided recommendations to improve robustness, interpretability, and clinical actionability of future adherence prediction models.