ReviewCureus2025
Artificial Intelligence in the Management of Polypharmacy Among Older Adults: A Scoping Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Perspectives of People with Serious Mental Illness on Artificial Intelligence-Based Companions: "If no one told you I love you today, you have someone that cares for you."Community mental health journal · 2026Article
- Appropriateness over Numbers: Pharmacological Burden and Scalable Medication Review in Older Adults.Geriatrics (Basel, Switzerland) · 2026Review
- Explainable Machine Learning for Predicting Adverse Drug Events in Older Adults with Polypharmacy: A Single-Center Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- Clinical-pharmacological medication reviews for insurants of a German statutory health insurance fund (Kaufmännische Krankenkasse (KKH)): a 5-year retrospective analysis (2019-2023).Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Early prediction and risk assessment of adverse drug combinations using ensemble learning.Scientific reports · 2026Article
- Advanced Drug Delivery Strategies in Geriatric Patients with Polypharmacy: Integrating Pharmacokinetics, Personalized Medicine, and Emerging Technologies.Journal of clinical medicine · 2026Review
- Polypharmacy, Medicine Optimisation, and Medication Adherence in Older Cancer Patients.Current oncology reports · 2026Review
- Review
- Artificial intelligence tool utilization in deprescribing practices among community pharmacists in Jordan: a cross-sectional study.Frontiers in digital health · 2026Article
- Artificial intelligence-driven risk prediction of polypharmacy in older adults: current advances, clinical applications, and future perspectives.Frontiers in public health · 2026Review
- The future of multimorbidity management in the older adults: transforming AI-enabled precision medicine.Frontiers in public health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of artificial intelligence (AI) presents an opportunity to enhance the management of multiple medications for patients. Despite the growing body of research on the clinical impact and accuracy of AI in healthcare, current literature is limited in addressing AI's role in medication management for older adults. The goal of this scoping review was to assess the extent and applicability of AI applications in managing multiple pharmacological therapies among adults aged 50 and older. Relevant articles were searched in EMBASE, Ovid MEDLINE, and Web of Science databases. Search phrases included "polypharmacy/polytherapy", "artificial intelligence in healthcare", "machine learning/language learning", and "elderly populations". The search initially identified 58 citations. After a systemized and rigorous screening process, 12 articles were further screened for eligibility and critically appraised for bias and appropriateness. Of those, a total of five articles were retained for the final analysis. They focused on the application of AI and web-based applications to reduce inappropriate medication use and drug interactions, how machine learning and audio-based activity recognition systems could improve medication adherence among older adults, and recognizable patterns between multimorbidity and polypharmacy in elderly populations with chronic illnesses. The main findings of this review suggested that AI tools have demonstrated efficiency and accuracy in eliminating drug-drug interactions, assisting in the detection of potentially inappropriate medications (PIMs), and identifying patterns of multimorbidity due to polypharmacy in older adults. Moreover, AI tools were considered easy to use and helped enhance medication adherence. Results suggest that AI has potential in managing polypharmacy, particularly in enhancing medication safety, improving adherence, and predicting risk factors for medication-related errors in older adults. Future investigations are needed that focus on AI's long-term effect on patient outcomes, its role in personalized pharmacotherapy, and ongoing challenges related to algorithmic transparency, bias mitigation, and regulatory oversight. As AI continues to evolve, its integration into healthcare would benefit from methodical evaluation, stringent oversight, and interdisciplinary collaboration to ensure its safe and effective deployment in patient care.
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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.