Evidence map›Paper›PMID 40526063›Full record

ReviewThe journals of gerontology. Series A, Biological sciences and medical sciences2025

Aging With Artificial Intelligence: How Technology Enhances Older Adults' Health and Independence.

Laura McDaniel, Ime Essien, Samuel Lefcourt, Ephrata Zelleke, Arushi Sinha, Rama Chellappa, Peter M Abadir

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in The journals of gerontology. Series A, Biological sciences and medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. 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

7 authors.

Laura McDanielDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Ime EssienDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Samuel LefcourtDepartment of Computer Science, Johns Hopkins University, Baltimore, Maryland, USA.
Ephrata ZellekeDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Arushi SinhaDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Rama ChellappaDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
Peter M AbadirDivision of Geriatrics and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0002-8186-0066

Funding

Technological Assessment and Solutions Core - RC4P30AG021334 · NIA · JOHNS HOPKINS UNIVERSITY · PI Jeremy D Walston · 2003 to 2026
$33.2M
Validating novel sleep sensors and devices in older adults with Alzheimer's diseaseP30AG073107 · NIA · UNIVERSITY OF MASSACHUSETTS AMHERST · PI Benjamin M. Marlin · 2021 to 2026
$32.0M
Utilizing Technology and AI Approaches to Facilitate Independence andResilience in Older AdultsP30AG073104 · NIA · JOHNS HOPKINS UNIVERSITY · PI Alexis Battle · 2021 to 2026
$31.2M
Technology Identification and Training CoreP30AG073105 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DEMIRIS, GEORGE, KARLAWISH, JASON H · 2021 to 2025
$21.2M
AI-Driven Frailty Assessment and Molecular Correlation: A Multimodal Mentorship InitiativeK24AG088484 · NIA · JOHNS HOPKINS UNIVERSITY · PI Peter M. Abadir · 2024 to 2026
$596k
Johns Hopkins University P30AG073107NIA NIH HHS K24 AG088484NIA NIH HHS K24AG088484NIA NIH HHS P30 AG021334NIA NIH HHS P30AG021334NIA NIH HHS P30 AG073104NIA NIH HHS P30 AG073105NIA NIH HHS P30 AG073107NIH HHS P30AG073104University of Massachusetts Amherst P30AG073105University of Pennsylvania
6 · The paper itself

Abstract

backgroundAs the global population ages healthcare challenges are escalating. Frailty, a clinical syndrome characterized by decreased reserve and resilience to stressors, is critically linked to adverse health outcomes in older adults. However, artificial intelligence (AI)-driven technologies offer promising solutions for revolutionizing older individuals care and enhancing senior health and independence.

objectiveThis paper explores how AI-driven technologies, including wearables, nonwearable devices, and wireless systems, are transforming senior care. These innovations enable continuous health monitoring, fall detection, medication adherence, and cognitive assistance. RECENT

findingsRecent advancements in sensor technology, machine learning/AI algorithms, and user interface design have made these technologies more effective and accessible to older adults. Key benefits include early health issue detection, improved medication adherence, reduced hospitalizations, extended independent living, and improved quality of life. Privacy concerns, ease of use, and technology adoption are challenges that must be addressed.

conclusionThoughtfully designed AI wearables and supportive policies and infrastructure can significantly enhance seniors' quality of life while reducing caregiver burden and healthcare costs. As technology advances, AI-driven solutions across wearable, nonwearable, and wireless devices are set to become indispensable in global strategies for healthy aging.

Indexed as

AgingArtificial IntelligenceIndependent LivingAccidental FallsAgedHumansQuality of LifeWearable Electronic DevicesWireless TechnologyArtificial intelligenceOlder adultsTechnology

Identifiers

PMID40526063
PMCPMC12202867

What OpenQuestion holds

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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 OpenQuestion graph.