Evidence map›Paper›PMID 38143345›Full record

ArticleJournal of Alzheimer's disease : JAD2024

What Does ChatGPT Know About Dementia? A Comparative Analysis of Information Quality.

Jill A Dosso, Jaya N Kailley, Julie M Robillard

Abstract readLetter
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

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

  1. Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    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

3 authors.

Jill A DossoDepartment of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada.
Jaya N KailleyDepartment of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada.
Julie M RobillardDepartment of Medicine, Division of Neurology, The University of British Columbia, Vancouver, British Columbia, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quality of information about dementia retrieved using ChatGPT is unknown. Content was evaluated for length, readability, and quality using the QUEST, a validated tool, and compared against online material from three North American organizations. Both sources of information avoided conflicts of interest, supported the patient-physician relationship, and used a balanced tone. Official bodies but not ChatGPT referenced identifiable research and pointed to local resources. Users of ChatGPT are likely to encounter accurate but shallow information about dementia. Recommendations are made for information creators and providers who counsel patients around digital health practices.

Indexed as

CaffeineDementiaDigital HealthHumansPhysician-Patient RelationsCaffeineAlzheimer’s diseaseartificial intelligencedementiahealth

Identifiers

PMID38143345
PMCPMC10836539

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.