Evidence map›Paper›PMID 41180382›Full record

ReviewAging medicine (Milton (N.S.W))2025

Artificial Intelligence in Medication Management for Older Adults in Low- and Middle-Income Countries: A Narrative Review.

Christina Malini Christopher, Nabin Pathak, Zaheer-Ud-Din Babar, Sunil Shrestha

Abstract readReview
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Christina Malini ChristopherDiscipline of Clinical Pharmacy, School of Pharmaceutical Science Universiti Sains Malaysia Gelugor, Penang Malaysia.
Nabin PathakDrug Information Unit and Pharmacovigilance Cell, Department of Pharmacy, Hetauda Hospital Madan Bhandari Academy of Health Sciences Hetauda, Makwanpur Nepal.ORCID https://orcid.org/0000-0002-1952-642X
Zaheer-Ud-Din BabarDepartment of Clinical Pharmacy & Practice, College of Pharmacy, QU Health Qatar University Doha Qatar.
Sunil ShresthaDepartment of Research and Academics Kathmandu Cancer Center Nala Road, Tathali Bhaktapur Nepal.ORCID https://orcid.org/0000-0002-9174-7120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

adherenceartificial intelligencehealthcare technologylow‐ and middle‐income countriesmedication managementolder adultspolypharmacy

Identifiers

PMID41180382
PMCPMC12576577

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.