Evidence map›Paper›PMID 41745281›Full record

ReviewJournal of fungi (Basel, Switzerland)2026

Can Artificial Intelligence Optimize the Early Diagnosis of Invasive Candidiasis? A Systematic Review and Meta-Analysis.

Hugo Almeida, Beatriz Rodríguez-Alonso, Montserrat Alonso-Sardón, Inmaculada Izquierdo, Ángela Romero-Alegría, Virginia Velasco-Tirado, Josué Pendones Ulerio, Javier Pardo Lledías, Moncef Belhassen-García

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of fungi (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Hugo AlmeidaServicio de Medicina Interna, Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca, 37007 Salamanca, Spain.ORCID 0000-0001-5454-1249
Beatriz Rodríguez-AlonsoServicio de Medicina Interna, Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca, 37007 Salamanca, Spain.
Montserrat Alonso-SardónCentro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), 37007 Salamanca, Spain.ORCID 0000-0003-1829-5858
Inmaculada IzquierdoServicio de Medicina Interna, Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca, 37007 Salamanca, Spain.
Ángela Romero-AlegríaServicio de Medicina Interna, Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca, 37007 Salamanca, Spain.
Virginia Velasco-TiradoCentro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), 37007 Salamanca, Spain.
Josué Pendones UlerioCentro de Investigación de Enfermedades Tropicales de la Universidad de Salamanca (CIETUS), 37007 Salamanca, Spain.ORCID 0000-0002-3580-0484
Javier Pardo LledíasServicio de Medicina Interna, Hospital Marqués de Valdecilla, 39008 Santander, Spain.ORCID 0000-0002-4439-7877
Moncef Belhassen-GarcíaServicio de Medicina Interna, Unidad de Enfermedades Infecciosas, Hospital Universitario de Salamanca, 37007 Salamanca, Spain.ORCID 0000-0002-2230-1256

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The early diagnosis of invasive candidiasis remains challenging in immunocompromised and other high-risk patients, prompting interest in artificial intelligence models for assisting clinical decision-making. We conducted a PROSPERO-registered systematic review and meta-analysis of artificial intelligence-based predictive models for the early identification of invasive

Indexed as

artificial intelligenceCandidacandidemiainvasive candidiasismachine learningpredictive models

Identifiers

PMID41745281
PMCPMC12942343

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.