Evidence mapPaperPMID 42130758Full record

ReviewAging medicine (Milton (N.S.W))2026

The Role of Artificial Intelligence in Medication Management for Older Adults: A Systematic Review.

Dipak Chandra Das, Moustaq Karim Khan Rony, Shovit Dutta, Md Sami Al Zubair Zujbe, Tapan Bhattacharjee, Niloy Debnath, Shabbir Abdullah Maruf, Chowdhury Galib Mortuza, Farhana Rahman, Akash Das

Abstract readReview
In one paragraph

Review in Aging medicine (Milton (N.S.W)), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Dipak Chandra DasPublic Health Foundation Bangladesh Dhaka Bangladesh.ORCID https://orcid.org/0009-0001-0566-8822
Moustaq Karim Khan RonyMiyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh.ORCID https://orcid.org/0000-0002-6905-0554
Shovit DuttaChattogram City Corporation Memon Maternity Hospital Chittagong Bangladesh.ORCID https://orcid.org/0009-0005-0411-3998
Md Sami Al Zubair ZujbeJamalpur Medical College Jamalpur Bangladesh.ORCID https://orcid.org/0009-0007-1831-105X
Tapan BhattacharjeeShahjalal University of Science and Technology Sylhet Bangladesh.ORCID https://orcid.org/0009-0009-7438-9294
Niloy DebnathChittagong Medical College Chittagong Bangladesh.ORCID https://orcid.org/0009-0000-8542-8185
Shabbir Abdullah MarufBangladesh University of Professionals Dhaka Bangladesh.ORCID https://orcid.org/0009-0007-9188-4698
Chowdhury Galib MortuzaBangladesh University of Health Sciences Dhaka Bangladesh.
Farhana RahmanShanto-Mariam University of Creative Technology Dhaka Bangladesh.ORCID https://orcid.org/0009-0005-9229-2999
Akash DasGonoshasthaya Samaj Vittik Medical College Dhaka Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Older adults face increased risks of medication non-adherence, adverse drug events, and polypharmacy due to chronic health conditions and complex drug regimens. Traditional medication management approaches often fall short in addressing these challenges. Artificial intelligence (AI) has emerged as a promising tool for enhancing medication safety and personalization in geriatric care. This systematic review aimed to explore the role of AI in medication management for older adults, highlighting its effectiveness, usability, ethical implications, and integration within healthcare systems. Following PRISMA guidelines, a comprehensive search was conducted across six databases (PubMed, Scopus, Web of Science, CINAHL, IEEE Xplore, and Cochrane Library) for studies published between January 2015 and March 2025. Eligible studies included qualitative, quantitative, and mixed-methods research on AI-based medication interventions for individuals aged 60 and above. Data were synthesized thematically. Twenty-nine studies were included. Five major themes emerged: (1) AI's ability to enhance adherence through smart reminders and automation; (2) personalized and predictive capabilities in managing complex regimens; (3) design and usability challenges among older adults; (4) ethical concerns related to trust, privacy, and autonomy; and (5) the importance of seamless integration within clinical workflows. Cross-cutting observations emphasized the need for hybrid care models, inclusive design, and digital literacy training. AI has the potential to transform geriatric medication management. However, its success depends on ethical implementation, user-centered design, healthcare integration, and attention to equity. Long-term evaluations are essential to ensure sustainable and inclusive outcomes.

Indexed as

artificial intelligencedigital health technologygeriatric caremedication managementolder adults

Identifiers

PMID42130758
PMCPMC13163944

What Socratic holds

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LicenceCC BY
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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 Socratic graph.