Evidence map›Paper›PMID 41994058›Full record

ArticleJournal of dentistry (Shiraz, Iran)2026

AI-Powered Microscopic Diagnostic Techniques for

Reyhaneh Shoorgashti, Farnaz Jafari, Simin Lesan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of dentistry (Shiraz, Iran), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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.

Reyhaneh ShoorgashtiDept. of Oral and Maxillofacial Medicine, School of Dentistry, Islamic Azad University of Medical Sciences, Tehran, Iran.
Farnaz JafariOral and Dental Diseases Research Center, Kerman University of Medical Sciences, Kerman, Iran.
Simin LesanDept. of Oral and Maxillofacial Medicine, School of Dentistry, Islamic Azad University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) powered technologies can help detect Purpose: This review explores recent advancements, methodologies, and clinical implications in the AI-driven microscopic detection of Materials and Method: A literature search was conducted across multiple databases, including PubMed, Scopus, Embase, Web of Science, and Google Scholar. Following a thorough review of the retrieved articles, 7 studies were selected for inclusion in this review. Results: This review analyzed 7 studies that employed AI and machine learning (ML) to detect the presence of Conclusion: AI can improve the detection of

Indexed as

Artificial IntelligenceCandida albicansDeep LearningMachine Learning

Identifiers

PMID41994058
PMCPMC13080348

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.