Evidence map›Paper›PMID 42808965›Full record

ArticleInternational journal of dermatology2026

AI Dermatology's Unfulfilled Promise: Why Early Melanoma Detection Remains Unverified.

Tim J Zeuner, Titus J Brinker

Abstract read
In one paragraph

Article in International journal of dermatology, 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

2 authors.

Tim J ZeunerDivision of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany.ORCID https://orcid.org/0009-0004-9367-3534
Titus J BrinkerDivision of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany.ORCID https://orcid.org/0000-0002-3620-5919

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A growing number of artificial intelligence (AI) systems for melanoma diagnosis now match dermatologist-level performance. Early detection is a driving factor, yet it is rarely validated beyond aggregate diagnostic accuracy. More targeted studies are necessary so these claims become verifiable and the promise of early detection can actually be delivered to patient care.

Indexed as

Artificial IntelligenceDermatologyEarly Detection of CancerMelanomaSkin NeoplasmsDermoscopyHumansartificial intelligenceBreslow thicknessdermoscopydiagnostic uncertaintyearly detectionexplainable AIhistopathologylongitudinal imagingmachine learningmelanoma

Identifiers

PMID42808965
PMCPMC13622394

What OpenQuestion holds

Textmetadata
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.