Implementation of AI Technology as An Intervention Strategy for Optimizing Medication Adherence
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Abstract
Treatment adherence is a crucial factor in the success of chronic disease therapy, but it remains a global challenge due to the limitations of traditional monitoring methods such as questionnaires and pill count, which are often biased and inaccurate. Non-adherence can exacerbate symptoms, increase rehospitalization, and burden healthcare costs. This study aims to map the modalities of Artificial Intelligence (AI) technology, its mechanisms of action, and its effectiveness as an intervention strategy to improve medication adherence. This study employs a narrative literature review, searching articles in the PubMed, ScienceDirect, and Springer Nature Link databases published between 2015–2025, using the keywords “Intervention,” “Artificial Intelligence,” and “Medication Adherence.” The inclusion criteria included research-based articles relevant to the topic, articles published within the last 10 years 2015–2025, and full-text articles in either Indonesian or English that were relevant to the topic. Of the 445 initial articles, seven met the inclusion criteria and were analyzed in terms of AI modalities, mechanisms of action, and effectiveness on medication adherence. The findings showed three dominant AI approaches: (1) Machine Learning-based Risk Prediction to identify patients at risk of non-adherence early on; (2) Computer Vision-based Visual Monitoring (VDOT) through smartphone applications and wearable devices to verify medication swallowing in real-time; and (3) Educational Support through Natural Language Processing (NLP)-based Chatbots and digital nudge interventions to provide empathetic education and behavioral encouragement. AI has been shown to increase adherence rates to >90%, reduce clinical symptoms, decrease recurrence rates, and lower healthcare costs by 25–32%. AI has proven to be an effective and comprehensive intervention strategy for optimizing medication adherence through real-time monitoring, risk prediction, and personalized behavioral support. However, the sustainability of implementation requires user-friendly application design, improved digital literacy, and privacy protection to ensure that the technology is acceptable and sustainable in clinical practice.
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