Evidence map›Paper›PMID 42269023›Full record

ArticleJMIR dermatology2026

Enhanced Diagnosis of Generalized Pustular Psoriasis With the Legit.Health Device as a Diagnosis Support Tool: Multireader Multicase Study.

Alfonso Medela, Ignacio Hernández Montilla, Alberto Sabater, Andy Aguilar, Taig Mac Carthy, Gurpreet Singh Chowdhry, Juan Semeco, Antonio Martorell

Abstract read
In one paragraph

Article in JMIR 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

8 authors.

Alfonso MedelaDepartment of Medical Data Science, Legit.Health, Gran Via 1 BAT Tower, Bilbao, 48001, Spain, 34 638127476.ORCID 0000-0001-5859-5439
Ignacio Hernández MontillaDepartment of Medical Data Science, Legit.Health, Gran Via 1 BAT Tower, Bilbao, 48001, Spain, 34 638127476.ORCID 0000-0003-0356-6619
Alberto SabaterDepartment of Medical Data Science, Legit.Health, Gran Via 1 BAT Tower, Bilbao, 48001, Spain, 34 638127476.ORCID 0000-0002-1156-8617
Andy AguilarDepartment of Clinical Endpoint Innovation, Legit.Health, Bilbao, Spain.ORCID 0000-0003-0618-6179
Taig Mac CarthyDepartment of Clinical Endpoint Innovation, Legit.Health, Bilbao, Spain.ORCID 0000-0001-5583-5273
Gurpreet Singh ChowdhryTA Inflammation, Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany.ORCID 0009-0007-7884-9337
Juan SemecoTA Inflammation Medicine, Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany.ORCID 0009-0008-4569-3425
Antonio MartorellDepartment of Clinical Endpoint Innovation, Legit.Health, Bilbao, Spain.ORCID 0000-0003-1378-1590

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Generalized pustular psoriasis (GPP) is a rare, chronic, systemic inflammatory disease with an unpredictable and heterogeneous clinical course characterized by chronic symptoms and periods of flaring. GPP presents diagnostic challenges due to its rarity and high similarity to other dermatologic diseases. Objective: This study aimed to assess the performance of Legit.Health, a medical device powered by artificial intelligence software, in assisting health care practitioners (HCPs) in identifying GPP. Methods: The medical device used in this study includes a deep neural network for skin disease recognition trained on thousands of images of over 200 skin conditions (classified based on the International Classification of Diseases, 11th Revision). The sensitivity and specificity of the algorithm for the differential diagnosis of GPP were assessed. Due to the scarcity of GPP-related images, the medical device was fine-tuned using a dataset that included 4397 new GPP images. Thereafter, 15 HCPs (n=11, 73.3% primary care practitioners and n=4, 26.7% dermatologists) prospectively reviewed a total of 100 images of 15 visually similar skin conditions virtually in a clinical setting. After their diagnostic prediction, Legit.Health provided a prompt giving them the top 5 possible skin conditions to assist them with their choice. Results: Legit.Health demonstrated high accuracy in identifying GPP, with top-1, top-3, and top-5 sensitivity and specificity of 0.80, 0.86, and 0.90 and 0.99, 0.99, and 0.96, respectively. Results showed a notable increase in the diagnostic accuracy of the HCPs with assistance from Legit.Health (P<.001), with an increase in GPP diagnostic accuracy of 22.97% overall, 24.24% for primary care practitioners, and 19.45% for dermatologists. Conclusions: This improvement highlights the potential of Legit.Health in assisting HCPs in diagnosing rare diseases such as GPP, particularly in primary care settings where expertise may be limited, thereby improving patient outcomes.

Indexed as

PsoriasisDiagnosis, DifferentialDigital HealthHumansSensitivity and SpecificityAIartificial intelligencediagnostic accuracygeneralized pustular psoriasishidradenitis suppurativapsoriasisrare diseases

Identifiers

PMID42269023
PMCPMC13252704

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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.