Evidence map›Paper›PMID 41912221›Full record

ReviewDermatology practical & conceptual2026

Effectiveness of Teledermatology on Clinical, Patient-Reported, and Operational Outcomes: A Systematic Review.

Mueed Ijaz, Rasi Mizori, Yasser Al Omran

Abstract readReview
In one paragraph

Review in Dermatology practical & conceptual, 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

3 authors.

Mueed IjazKing's College London, UK.
Rasi MizoriKing's College London, UK.
Yasser Al OmranThe Royal Marsden NHS Trust, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTeledermatology (TD), the remote diagnosis and management of skin conditions using digital platforms, has rapidly evolved since its initial adoption in 1995. With the onset of the COVID-19 pandemic, the utilization of TD expanded significantly, offering a viable alternative to face-to-face (F2F) consultations, particularly in settings where access to dermatologists are limited.

objectivesThis systematic review aimed to evaluate the effectiveness of TD across three key domains: clinical outcomes, patient satisfaction, and cost-effectiveness.

methodsA systematic search was conducted across MEDLINE, Embase, and Web of Science for studies published between 2010 and July 2024. Studies comparing TD with in-person consultations in terms of diagnostic accuracy, patient satisfaction, and cost-effectiveness were included. The quality of the included studies were as assessed using the Newcastle-Ottawa Scale (NOS).

resultsFrom 2,768 articles, 23 studies met the inclusion criteria. Clinical outcomes indicated moderate agreement between TD and F2F consultations, with a mean kappa coefficient of 0.57, reflecting diagnostic concordance. Patient satisfaction varied widely, with 26.57% of patients willing to replace F2F consultations with TD. TD was associated with significant cost savings, averaging US$81.31 per patient, with percentage savings ranging from 6.27% to 45.33%, depending on the healthcare system.

conclusionsTD provides moderate diagnostic accuracy, substantial cost savings, and varying degrees of patient acceptance. However, successful implementation requires high-quality imaging, clinician training, and robust technical infrastructure. Further research is needed to optimize TD's role in dermatology, particularly in balancing virtual and F2F care.

Identifiers

PMID41912221
PMCPMC12952809

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.