ReviewAging medicine (Milton (N.S.W))2025
Artificial Intelligence in Medication Management for Older Adults in Low- and Middle-Income Countries: A Narrative Review.
Review in Aging medicine (Milton (N.S.W)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Explainable Machine Learning for Predicting Adverse Drug Events in Older Adults with Polypharmacy: A Single-Center Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Advanced Drug Delivery Strategies in Geriatric Patients with Polypharmacy: Integrating Pharmacokinetics, Personalized Medicine, and Emerging Technologies.Journal of clinical medicine · 2026Review
- Review
- Embracing the Digital Revolution: How Artificial Intelligence is Transforming Clinical Trials in Older Participants.Drugs & aging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) has emerged as a transformative tool in medication management, particularly for older adults who are vulnerable to adverse drug events, polypharmacy, and medication non-adherence. This narrative review explores the utilization of AI-driven interventions in low and middle-income countries (LMICs) to enhance medication safety, adherence, and prescribing practices. The review synthesizes existing studies on AI applications, including automated drug interaction detection, machine learning models for adverse event prediction, and AI-supported decision-making tools for healthcare professionals. Findings suggest that AI has demonstrated significant potential in reducing inappropriate medication use and improving patient adherence through mobile applications and electronic health record (EHR) integration. However, AI adoption in LMICs remains limited despite its benefits due to high implementation costs, insufficient digital infrastructure, low AI literacy among healthcare providers, and ethical concerns related to data privacy and algorithm bias. Addressing these barriers requires strategic policy reforms, investment in AI education, and improved regulatory frameworks to ensure responsible and equitable AI deployment. Future research should focus on evaluating the long-term effectiveness of AI interventions in real-world settings and developing scalable solutions tailored to LMICs. With the right support, AI has the potential to revolutionize medication management, improving the quality of care and health outcomes for older adults globally.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.