Evidence mapPaperPMID 41398578Full record

ReviewBMC geriatrics2025

Smart aging: integrating AI into elderly healthcare.

Claudio Tana, Carmine Siniscalchi, Nicoletta Cerundolo, Tiziana Meschi, Paolo Martelletti, Marco Tana, Livia Moffa, William Wells-Gatnik, Francesco Cipollone, Maria Adele Giamberardino

Abstract readReview
In one paragraph

Review in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. 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

10 authors.

Claudio TanaInternal Medicine Unt, Eastern Hospital, ASL Taranto, Italy. claudio.tana@yahoo.it.
Carmine SiniscalchiDepartment of Internal Medicine, University Hospital of Parma, Parma, Italy.
Nicoletta CerundoloDepartment of Internal Medicine, University Hospital of Parma, Parma, Italy.
Tiziana MeschiDepartment of Internal Medicine, University Hospital of Parma, Parma, Italy.
Paolo MartellettiSchool of Health, Unitelma Sapienza University of Rome, Rome, Italy.
Marco TanaInternal Medicine Unit, S. Annunziata Hospital of Chieti, "G. D'Annunzio" University of Chieti, Chieti, Italy.
Livia MoffaInfectious Disease Unit, University Hospital of Chieti, Chieti, Italy.
William Wells-GatnikSchool of Health, Unitelma Sapienza University of Rome, Rome, Italy.
Francesco CipolloneDepartment of Medicine and Science of Aging, Medical Clinic, SS. Annunziata Hospital of Chieti, "G. D'Annunzio" University of Chieti, Chieti, Italy.
Maria Adele GiamberardinoDepartment of Medicine and Science of Aging and CAST, Geriatrics Clinic, SS Annunziata Hospital of Chieti, and G. D'Annunzio University of Chieti, Chieti, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is transforming geriatric healthcare by improving early disease detection, optimizing patient management, and enhancing clinical decision-making. AI-driven tools, including machine learning (ML) algorithms, predictive analytics, and assistive robotics, are increasingly utilized to address challenges associated with aging, such as frailty, multimorbidity, polypharmacy, and fall prevention. These technologies can facilitate personalized medicine, improve diagnostic accuracy, and support healthcare administration by streamlining workflows and resource allocation. Despite its promising applications, the integration of AI in geriatric care presents several challenges, including issues related to data privacy, algorithmic bias, ethical considerations, and the digital literacy of older adults. Ensuring the inclusivity of AI models by incorporating diverse and representative datasets is essential to avoid disparities in healthcare delivery. Additionally, the role of AI must be carefully regulated to complement, rather than replace, human clinical expertise. This paper provides a comprehensive overview of AI applications in geriatric medicine, discussing its benefits, limitations, and future directions. By leveraging AI responsibly, healthcare systems can improve patient outcomes, reduce hospital readmissions, and promote aging in place. However, addressing existing challenges through interdisciplinary collaboration, policy development, and continued research is crucial to fully realizing AI's potential in elderly care.

Indexed as

AgingArtificial IntelligenceDelivery of Health CareGeriatricsHealth Services for the AgedAgedHumansArtificial intelligenceAssistive roboticsCardiovascular diseasesElderly careFrailtyMachine learningOlder adultsSmart home

Identifiers

PMID41398578
PMCPMC12706947

What Socratic 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 Socratic graph.