Evidence map›Paper›PMID 42656918›Full record

ArticleAlzheimer's & dementia. Behavior & socioeconomics of aging2025

Artificial intelligence as a tool for cognitive impairment screening: Patient perspectives about benefits and limitations.

Heather M Wurtz, Margaret Manchester, Tatyana Valteau, Hannah Hanson, Catherine Scipion, Alex Federman, Jalayne J Arias

Abstract read
In one paragraph

Article in Alzheimer's & dementia. Behavior & socioeconomics of aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Real-world prediction of early-onset dementia by health record data: A multi-center machine learning study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    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.

Heather M WurtzDepartment of Anthropology and Human Rights Institute, University of Connecticut, Storrs, Connecticut, USA.
Margaret ManchesterDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.ORCID 0000-0003-4041-9300
Tatyana ValteauDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.
Hannah HansonDivision of General Internal Medicine, Icahn School of Medicine at Mt. Sinai, New York, New York, USA.
Catherine ScipionDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.
Alex FedermanDivision of General Internal Medicine, Icahn School of Medicine at Mt. Sinai, New York, New York, USA.
Jalayne J AriasDepartment of Health Policy and Behavioral Sciences, School of Public Health, Georgia State University, Atlanta, Georgia, USA.

Funding

Natural Language Processing and Automated Speech Recognition to Identify Older Adults with Cognitive Impairment SupplementR01AG066471 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI FEDERMAN, ALEX D, WISNIVESKY, JUAN P · 2020 to 2024
$4.5M
NIA NIH HHS R01 AG066471
6 · The paper itself

Abstract

Introduction: This paper presents findings from a qualitative study exploring the perspectives of primary care patients on the use of artificial intelligence (AI) to synthesize data from the electronic medical record (EMR) and audio recordings of patient speech as a way to detect cognitive impairment. Methods: We conducted qualitative interviews with a racially and educationally diverse sample of primary care patients ( Results: Participants reported that AI technology could improve diagnostic accuracy and avoid human errors in the evaluation and diagnosis of cognitive impairment, while also improving clinical efficiency for clinicians. They also expressed concerns about the use of AI tools for screening, such as risk of misdiagnosis, loss of confidentiality, and psychological distress from learning of detected cognitive impairment. Conclusion: The perceived benefits reported by participants regarding the use of AI screening tools suggest that some patients might support the provision of AI screening in clinical settings. However, participants' concerns about the potential harms of AI screening tools underscore the need for clear and transparent communication about how results are generated and interpreted. Clinicians need training and other forms of structural support to improve their literacy in AI-driven tools and their ability to integrate adequate approaches to information sharing into clinical practice.

Indexed as

artificial intelligencecognitive impairmentpatient perspectivesscreening

Identifiers

PMID42656918
PMCPMC13508175

What OpenQuestion holds

Textmetadata
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.