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SYSTEMATIC REVIEW article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1523070
This article is part of the Research TopicMathematical Modeling of Medication NonadherenceView all articles
Artificial Intelligence-based Tools for Patient Support to Enhance Medication Adherence: A Focused Review
Provisionally accepted
Zilma Silveira Nogueira Reis1*
Gláucia Miranda Varella Pereira2
Cristiane dos Santos Dias3
Eura Martins Lage4Isaias José Ramos de Oliveira1Adriana Silvina Pagano5- 1Health Informatics Center, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- 2State University of Campinas, Campinas, São Paulo, Brazil
- 3Department of Pediatrics, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
- 4Department of Gynecology and Obstetrics, Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Mato Grosso, Brazil
- 5Arts Faculty, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
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Objective: Medication adherence involves patients correctly taking medications as prescribed. This review evaluates whether artificial intelligence (AI) based tools contribute to adherence-related insights or avoid medication intake errors.Methods: We assessed studies employing AI tools to directly benefit patient medication use, promoting adherence or avoiding self-administration error outcomes. The search strategy was conducted on six databases in August 2024. ROB2 and ROBINS1 assessed the risk of bias.Results: The review gathered seven eligible studies, including patients from three clinical trials and one prospective cohort. The overall risk of bias was moderate to high. Three reports drew on conceptual frameworks with simulated testing. The evidence identified was scarce considering measurable outcomes. However, based on randomized clinical trials, AI-based tools improved medication adherence ranging from 6.7% to 32.7% compared to any intervention controls and current practices, respectively. Digital intervention using video and voice interaction providing real-time monitoring pointed to AI's potential to alert to self-medication errors. Based on conceptual framework reports, we highlight the potential of cognitive behavioral approaches tailored to engage patients in their treatment.Conclusion: Even though the present evidence is weak, smart systems using AI tools are promising in helping patients use prescribed medications. The review offers insights for future research.
Keywords: Prescriptions, machine learning, artificial intelligence, Directive Counseling, Medication Adherence
Received: 05 Nov 2024; Accepted: 09 Apr 2025.
Copyright: © 2025 Reis, Pereira, Dias, Lage, de Oliveira and Pagano. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zilma Silveira Nogueira Reis, Health Informatics Center, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.