Evidence map›Paper›PMID 35515814›Full record

SynthesisThe lancet. Healthy longevity2022

Artificial intelligence for older people receiving long-term care: a systematic review of acceptability and effectiveness studies.

Kate Loveys, Matthew Prina, Chloe Axford, Òscar Ristol Domènec, William Weng, Elizabeth Broadbent, Sameer Pujari, Hyobum Jang, Zee A Han, Jotheeswaran Amuthavalli Thiyagarajan

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in The lancet. Healthy longevity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 2 pooled it
5.6field-weighted citation impact, top 4% of its field
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

31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 92 citations in OpenAlex.

  1. Pooled it
  2. AI Quality Standards in Health Care: Rapid Umbrella Review.Journal of medical Internet research · 2024
    Pooled it
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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 at 4 institutions in 4 countries.

Kate LoveysDepartment of Psychological Medicine, University of Auckland, Auckland, New Zealand.
Matthew PrinaDepartment of Health Services and Population Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Chloe AxfordDepartment of Health Services and Population Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Òscar Ristol DomènecDepartment of Health Services and Population Research, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
William WengStanford Prevention Research Center, Department of Medicine, Stanford University, Stanford, CA, USA.
Elizabeth BroadbentDepartment of Psychological Medicine, University of Auckland, Auckland, New Zealand.
Sameer PujariDepartment of Digital Health, World Health Organization, Geneva, Switzerland.
Hyobum JangDepartment of Maternal, Newborn, Child and Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland.
Zee A HanDepartment of Maternal, Newborn, Child and Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland.
Jotheeswaran Amuthavalli ThiyagarajanDepartment of Maternal, Newborn, Child and Adolescent Health and Ageing, World Health Organization, Geneva, Switzerland.
World Health Organization · CHKing's College London · GBUniversity of Auckland · NZStanford University · US

Funding

World Health Organization 001
6 · The paper itself

Abstract

Artificial intelligence (AI)-enhanced interventions show promise for improving the delivery of long-term care (LTC) services for older people. However, the research field is developmental and has yet to be systematically synthesised. This systematic review aimed to synthesise the literature on the acceptability and effectiveness of AI-enhanced interventions for older people receiving LTC services. We conducted a systematic search that identified 2720 records from Embase, Ovid, Global Health, PsycINFO, and Web of Science. 31 articles were included in the review that evaluated AI-enhanced social robots (n=22), environmental sensors (n=6), and wearable sensors (n=5) with older people receiving LTC services across 15 controlled and 14 non-controlled trials in high-income countries. Risk of bias was evaluated using the RoB 2, RoB 2 CRT, and ROBINS-I tools. Overall, AI-enhanced interventions were found to be somewhat acceptable to users with mixed evidence for their effectiveness across different health outcomes. The included studies were found to have high risk of bias which reduced confidence in the results. AI-enhanced interventions are promising innovations that could reshape the landscape of LTC globally. However, more trials are required to support their widespread implementation. Pathways are needed to support more high-quality trials, including in low-income and middle-income countries.

Indexed as

Artificial IntelligenceLong-Term CareAgedGlobal HealthHumans

Identifiers

PMID35515814
PMCPMC8979827
OpenAlexW4225880178

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.