Evidence map›Paper›PMID 40686623›Full record

ReviewMayo Clinic proceedings. Digital health2025

Artificial Intelligence in Digital Self-Diagnosis Tools: A Narrative Overview of Reviews.

Aikaterini Mentzou, Amy Rogers, Edzia Carvalho, Angela Daly, Maeve Malone, Xaroula Kerasidou

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Mayo Clinic proceedings. Digital health, 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. Article
  2. QHealthcare (Basel, Switzerland) · 2026
    Article
  3. 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

6 authors.

Aikaterini MentzouDivision of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, UK.
Amy RogersDivision of Cardiovascular Medicine, School of Medicine, University of Dundee, UK.
Edzia CarvalhoPolitics and International Relations, School of Humanities, Social Sciences and Law, University of Dundee, UK.
Angela DalyLeverhulme Research Centre for Forensic Science, University of Dundee, UK.
Maeve MaloneDundee Law School, School of Humanities, Social Sciences and Law, University of Dundee, UK.
Xaroula KerasidouEthox Centre, Oxford Population Health (Nuffield Department of Population Health), Big Data Institute, Li Ka Shing Centre for Health Information and Discovery Oxford, University of Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital self-diagnosis tools, or symptom checkers, many of which incorporate artificial intelligence technology, are intended to provide diagnostic information and triage advice to lay users. This narrative overview of reviews explores the common themes and issues raised by existing evidence synthesis literature on these tools to establish a common ground for interdisciplinary research. We searched 3 bibliographic databases (PubMed, Scopus and Web of Science) and Google Scholar using keyword combinations of

Identifiers

PMID40686623
PMCPMC12271431

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.