Artificial Intelligence in Medication Adherence and Personalized Treatment Plans

Adherence to medication and personalized treatment constitute the cornerstones of effective chronic disease management, yet the realization of their intent remains challenged by patient-specific, therapeutic, and systemic barriers. Due to the advancing availability of big health data and computational techniques, AI has made its stride into the health scenario as a disruptive force. This review discusses a variety of AI-powered tools used to monitor and enhance medication adherence—smart pill bottles, ingestible sensors, wearables, mHealth apps, and predictive analytics—and their incorporation into real-time clinical decision-making. We further discuss AI's role in developing personalized treatment regimens based on genomic data, adaptive algorithms, and electronic health records (EHR). The ethics of these challenges, including data privacy, algorithmic bias, and accountability, are further elucidated, alongside issues of in-field implementation. Lastly, along with the topics mentioned earlier, the review talks about future ideas like explainable AI, federated learning, and closed-loop adherence systems, which could help make AI a key part of personalized, proactive, and fair healthcare.