Keywords
medication adherence
nursing
chronic disease
digital health
predictive analytics
chatbot
How to Cite
Abstract
Background. Therapeutic non-adherence affects approximately half of all patients with chronic conditions and is a leading driver of preventable morbidity, hospital readmission and excess healthcare expenditure. As nurses are positioned at the front line of patient education, monitoring and follow-up, the rapid emergence of artificial intelligence (AI) tools has direct relevance for everyday nursing practice. Objectives. To synthesize current evidence on how AI-based solutions, including machine-learning risk-prediction models, conversational agents, smart pill devices and tailored digital coaching, may influence therapeutic adherence, and to discuss the implications for contemporary nursing practice. Material and methods. A narrative review was conducted using PubMed, CINAHL, Scopus and Web of Science. Studies published between 2017 and 2025 addressing AI applications in medication or treatment adherence were selected, with priority given to systematic reviews, randomized clinical trials and nursing-relevant evidence. Results. AI-based interventions improved medication adherence by approximately 6.7% to 32.7% compared with usual care in randomized trials. Predictive models identified high-risk non-adherent patients with an area under the curve up to 0.87, allowing earlier and better- -targeted nursing interventions. Conversational agents enhanced patient engagement and self-management in chronic conditions such as diabetes, hypertension and heart failure, while reducing readmissions by up to 25% in some studies. Persistent barriers include data quality, algorithmic bias, digital health literacy and ethical concerns about patient autonomy and confidentiality. Conclusions. AI offers a meaningful opportunity to support nurse-led adherence strategies through earlier risk identification, personalized education and continuous remote monitoring. To realize its potential, nurses require structured training in digital health, professional standards must address ethical and legal aspects, and AI tools should be designed as a complement to, not a substitute for, the therapeutic nurse-patient relationship.References
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