Evidence map›Paper›PMID 42633241›Full record

ArticleJAAD international2026

An explainable deep learning model classifies eight categories of pigmented skin lesions on clinical photographs: A multicenter retrospective internal validation study in Japan.

Kimi Iinuma, Kazuyasu Fujii, Chisa Nakashima, Kenichiro Kasai, Hiroyuki Irie, Hitonari Kanetomo, Shigeto Yanagihara, Sayuri Sato, Hisashi Uhara, Fumiaki Takeda and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JAAD international, 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

13 authors.

Kimi IinumaDepartment of Dermatology, Kindai University Hospital, Sakai, Japan.
Kazuyasu FujiiDepartment of Dermatology, Kindai University Hospital, Sakai, Japan.
Chisa NakashimaDepartment of Dermatology, Kindai University Hospital, Sakai, Japan.
Kenichiro KasaiKasai Clinic for Plastic Surgery, Osaka, Japan.
Hiroyuki IrieDepartment of Dermatology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Hitonari KanetomoKanetomo Dermatology Clinic, Osaka, Japan.
Shigeto YanagiharaKanetomo Dermatology Clinic, Osaka, Japan.
Sayuri SatoDepartment of Dermatology, Sapporo Medical University School of Medicine, Sapporo, Japan.
Hisashi UharaDepartment of Dermatology, Sapporo Medical University School of Medicine, Sapporo, Japan.
Fumiaki TakedaHiroshima University Digital Monozukuri (Manufacturing) Education and Research Center, Hiroshima, Japan.
Yuichi KimuraFaculty of Informatics, Cyber Informatics Research Institute, Kindai University, Osaka, Japan.
Takashi NagaokaFaculty of Biology-Oriented Science and Technology, Kindai University, Osaka, Japan.
Atsushi OtsukaDepartment of Dermatology, Kindai University Hospital, Sakai, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

artificial intelligenceclinical photographsdeep learningexplainabilitymelanomapigmented skin lesions

Identifiers

PMID42633241
PMCPMC13499408

What OpenQuestion holds

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Registered trials

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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.