Evidence map›Paper›PMID 41118658›Full record

ArticleJMIR aging2025

Using Artificial Intelligence-Based Technologies for the Early Detection of Behavioral and Psychological Symptoms of Dementia: Scoping Review.

Sofia Fernandes, Joëlle Rosselet Amoussou, Carla Gomes da Rocha, Elodie Perruchoud, Armin von Gunten, Cédric Mabire, Henk Verloo

Abstract readScoping Review
In one paragraph

Article in JMIR aging, 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. Trial
  2. Review
  3. Article
  4. Article
  5. Article
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.

Sofia FernandesSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis, Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 58 606 86 17.ORCID http://orcid.org/0000-0003-4541-1041
Joëlle Rosselet AmoussouMedical Library-Cery, Lausanne University Hospital and University of Lausanne, Site de Cery, Prilly, Switzerland.ORCID http://orcid.org/0000-0001-6871-5350
Carla Gomes da RochaSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis, Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 58 606 86 17.ORCID http://orcid.org/0000-0002-6302-7732
Elodie PerruchoudSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis, Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 58 606 86 17.ORCID http://orcid.org/0000-0002-5436-3179
Armin von GuntenService of Old Age Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.ORCID http://orcid.org/0000-0001-7852-3803
Cédric MabireLausanne University Hospital and University of Lausanne Faculty of Biology and Medicine, Institute of Higher Education and Research in Healthcare, Lausanne, Switzerland.ORCID http://orcid.org/0000-0003-2666-8300
Henk VerlooSchool of Health Sciences, University of Applied Sciences and Arts Western Switzerland (HES-SO) Valais-Wallis, Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 58 606 86 17.ORCID http://orcid.org/0000-0002-5375-3255

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: People with dementia commonly display behavioral and psychological symptoms, which have multiple negative consequences. Artificial intelligence-based technologies (AITs) have the potential to support earlier detection of the behavioral and psychological symptoms of dementia (BPSD). The recent surge of interest in this topic underscores the need to comprehensively examine the existing evidence. Objective: This scoping review aimed to identify and summarize the types and uses of AITs currently used for the early detection of BPSD among people diagnosed with the disease. We also examined which health care professionals were involved, nursing involvement and experience, the care settings in which these technologies are used, and the characteristics of the BPSD that were assessed. Methods: Our scoping review was conducted in accordance with the Joanna Briggs Institute manual for scoping reviews. Searches were conducted in March 2025 in the following bibliographic databases: MEDLINE ALL Ovid, Embase, APA PsycINFO Ovid, CINAHL EBSCO, Web of Science Core Collection, the Cochrane Library Wiley, and ProQuest Dissertations and Theses A&I. Additional searches were performed using citation tracking strategies and by consulting the Association for Computing Machinery Digital Library. Eligible studies included primary research involving people with dementia and examining the use of AITs for the detection of BPSD in real-world care settings. Results: After screening 3670 articles for eligibility, the review includes 12 studies. The studies retained were conducted between 2012 and 2025 in 5 countries and encompassed a range of care settings. The AITs used were predominantly based on classic machine learning approaches and used information from environmental sensors, wearable devices, and data recording systems. These studies primarily assessed behavioral and physiological parameters and focused specifically on symptoms, such as agitation and aggression. None of the retained studies explored nurses' roles or their specific skills in using these technologies. Conclusions: The use of AITs for managing BPSD represents an emerging field of research offering novel opportunities to enhance their detection in various health care contexts. We recommended that nurses be actively engaged in developing and assessing these technologies. Future research should prioritize investigations into how effective AITs are across diverse populations, whether they can have a long-term impact on managing BPSD, and whether they can improve the quality of life of patients and caregivers.

Indexed as

Artificial IntelligenceDementiaEarly DiagnosisHumansartificial intelligencebehavioral and psychological symptoms of dementiaearly detectionolder adultsscoping review

Identifiers

PMID41118658
PMCPMC12539798

What Socratic 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 Socratic graph.