Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review
ObjectiveMedication 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.MethodsWe assessed studies employing AI tools to directly bene...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Digital Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1523070/full |
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| author | Zilma Silveira Nogueira Reis Gláucia Miranda Varella Pereira Cristiane dos Santos Dias Eura Martins Lage Isaias José Ramos de Oliveira Adriana Silvina Pagano |
| author_facet | Zilma Silveira Nogueira Reis Gláucia Miranda Varella Pereira Cristiane dos Santos Dias Eura Martins Lage Isaias José Ramos de Oliveira Adriana Silvina Pagano |
| author_sort | Zilma Silveira Nogueira Reis |
| collection | DOAJ |
| description | ObjectiveMedication 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.MethodsWe 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.ResultsThe 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.ConclusionEven 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.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024571504, identifier: CRD42024571504. |
| format | Article |
| id | doaj-art-fc36543e8b844dbaae8dcbda879e5e27 |
| institution | Kabale University |
| issn | 2673-253X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj-art-fc36543e8b844dbaae8dcbda879e5e272025-08-20T03:53:27ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2025-04-01710.3389/fdgth.2025.15230701523070Artificial intelligence-based tools for patient support to enhance medication adherence: a focused reviewZilma Silveira Nogueira Reis0Gláucia Miranda Varella Pereira1Cristiane dos Santos Dias2Eura Martins Lage3Isaias José Ramos de Oliveira4Adriana Silvina Pagano5Health Informatics Center, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilDepartment of Obstetrics and Gynecology, Faculty of Medical Sciences, Universidade Estadual de Campinas, Campinas, BrazilDepartment of Pediatrics, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilDepartment of Gynecology and Obstetrics, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilHealth Informatics Center, Faculty of Medicine, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilArts Faculty, Universidade Federal de Minas Gerais, Belo Horizonte, BrazilObjectiveMedication 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.MethodsWe 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.ResultsThe 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.ConclusionEven 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.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD42024571504, identifier: CRD42024571504.https://www.frontiersin.org/articles/10.3389/fdgth.2025.1523070/fullprescriptionsmachine learningartificial intelligencedirective counselingmedication adherence |
| spellingShingle | Zilma Silveira Nogueira Reis Gláucia Miranda Varella Pereira Cristiane dos Santos Dias Eura Martins Lage Isaias José Ramos de Oliveira Adriana Silvina Pagano Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review Frontiers in Digital Health prescriptions machine learning artificial intelligence directive counseling medication adherence |
| title | Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review |
| title_full | Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review |
| title_fullStr | Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review |
| title_full_unstemmed | Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review |
| title_short | Artificial intelligence-based tools for patient support to enhance medication adherence: a focused review |
| title_sort | artificial intelligence based tools for patient support to enhance medication adherence a focused review |
| topic | prescriptions machine learning artificial intelligence directive counseling medication adherence |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2025.1523070/full |
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