Evidence map›Paper›PMID 39594976›Full record

ReviewChildren (Basel, Switzerland)2024

Perspectives and Challenges of Telemedicine and Artificial Intelligence in Pediatric Dermatology.

Daniele Zama, Andrea Borghesi, Alice Ranieri, Elisa Manieri, Luca Pierantoni, Laura Andreozzi, Arianna Dondi, Iria Neri, Marcello Lanari, Roberta Calegari

Registry-linked trialAbstract readReview
In one paragraph

Review in Children (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07705386 (Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis), which is not on this map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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.

NCT07705386 active not recruitingnot on this mapstarted 2026, after this paper: background citation

Development of an AI-Assisted Diagnostic Tool for Mycosis Fungoides and Other Cutaneous Lymphoproliferative Diseases Using Microscopic Image Analysis: A Training and Validation Study

TypeobservationalSponsorCairo UniversityRan2026 to 2026Enrolled463ConditionsCutaneous Lymphoproliferative Diseases, Mycosis Fungoides of Skin (Diagnosis), T Cell Dyscrasia, PLEVA-PLC SpectrumArmsAI-assisted histopathology image analysis
3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
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  6. Review
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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

10 authors.

Daniele ZamaDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy.ORCID 0000-0002-9895-5942
Andrea BorghesiDepartment of Computer Science and Engineering (DISI), Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy.ORCID 0000-0002-2298-2944
Alice RanieriSpecialty School of Paediatrics, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy.ORCID 0009-0003-7378-7713
Elisa ManieriSpecialty School of Paediatrics, Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy.ORCID 0000-0003-3150-7528
Luca PierantoniPediatric Emergency Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.ORCID 0000-0001-8175-5488
Laura AndreozziPediatric Emergency Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.ORCID 0000-0001-7755-9652
Arianna DondiDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy.ORCID 0000-0002-7516-243X
Iria NeriDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy.
Marcello LanariDepartment of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, 40138 Bologna, Italy.
Roberta CalegariDepartment of Computer Science and Engineering (DISI), Alma Mater Studiorum, University of Bologna, 40126 Bologna, Italy.ORCID 0000-0003-3794-2942

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPediatric dermatology represents one of the most underserved subspecialties in pediatrics. Artificial intelligence (AI) and telemedicine have become considerable in dermatology, reaching diagnostic accuracy comparable to or exceeding that of in-person visits. This work aims to review the current state of telemedicine and AI in pediatric dermatology, suggesting potential ways to address existing issues and challenges.

methodsWe conducted a literature review including only articles published in the last 15 years. A total of 458 studies were identified, of which only 76 were included.

resultsMost of the studies on telemedicine evaluate accuracy focused on concordance, which ranges from 70% to 89% for the most common pediatric skin diseases. Telemedicine showed the potential to manage chronic dermatological conditions in children, as well as decrease waiting times, and represents the chance for unprivileged populations to overcome barriers limiting access to medical care. The main limitations of telemedicine consist of the language barrier and the need for adequate technologies and acceptable image-quality video, which can be overcome by AI. AI-driven apps and platforms can facilitate remote consultations between pediatric dermatologists and patients or their caregivers. However, the integration of AI into clinical practice faces some challenges ranging from technical to ethical and regulatory. It is crucial to ensure that the development, deployment, and utilization of AI systems conform to the seven fundamental requirements for trustworthy AI.

conclusionThis study supplies a detailed discussion of open challenges with a particular focus on equity and ethical considerations and defining possible concrete directions.

Indexed as

artificial intelligencechildrendermatologistspediatric dermatologytelemedicine

Identifiers

PMID39594976
PMCPMC11592520

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.