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

Published in JMIR Dermatology, this peer-reviewed multireader multicase (MRMC) study evaluates how Legit.Health, an AI-based medical device, supports health care practitioners in identifying generalized pustular psoriasis (GPP), a rare and difficult-to-diagnose condition.

Medela A, Hernández Montilla I, Sabater A, Aguilar A, Mac Carthy T, Chowdhry GS, Semeco J, Martorell A. Enhanced Diagnosis of Generalized Pustular Psoriasis With the Legit.Health Device as a Diagnosis Support Tool: Multireader Multicase Study. JMIR Dermatol. 2026;9:e82030. https://doi.org/10.2196/82030

Summary

• Study type: Multireader multicase (MRMC) study
• Journal: JMIR Dermatology (2026)
• DOI: https://doi.org/10.2196/82030
• Readers: 15 health care practitioners (11 primary care practitioners and 4 dermatologists)
• Cases: 100 images of GPP and visually similar conditions, reviewed first without device assistance and then with the device's top 5 predictions
• Objective: Assess whether the Legit.Health device improves the accuracy of health care practitioners in diagnosing generalized pustular psoriasis

Background and rationale

Generalized pustular psoriasis is a rare, chronic, systemic inflammatory disease with an unpredictable clinical course. Its rarity and visual similarity to other dermatologic conditions make it challenging to diagnose, which can delay appropriate treatment. Artificial intelligence presents a transformative opportunity to enhance the diagnostic capabilities of health care practitioners, particularly in primary care settings where specialized dermatologic expertise may not be available.

Legit.Health is an AI-based medical device incorporating a deep neural network trained on more than 200 skin conditions. This study assessed whether the device, used as a diagnosis support tool, can increase the accuracy of health care practitioners in identifying GPP.

Design

The device was fine-tuned on 4397 GPP images and then evaluated by 15 health care practitioners (11 primary care practitioners and 4 dermatologists). Each reader reviewed 100 images of GPP and visually similar conditions in two stages: first without device assistance, and then with the device's top 5 predictions. Diagnostic accuracy was compared between the two stages to measure the impact of the device as a diagnosis support tool.

Results

Device standalone performance

The device was fine-tuned on 4397 GPP images. The table below reports its diagnostic performance as a standalone classifier before and after fine-tuning.

Metric	Before fine-tuning	After fine-tuning
Top-1 sensitivity	0.4000	0.8026
Top-1 specificity	1.0000	0.9983
Top-3 sensitivity	0.8000	0.8633
Top-3 specificity	0.9994	0.9957
Top-5 sensitivity	0.8000	0.9002
Top-5 specificity	0.9986	0.9611

After fine-tuning, the device reached top-1, top-3, and top-5 sensitivity of 0.80, 0.86, and 0.90, respectively, while keeping specificity above 0.90 across all thresholds.

Improvement in GPP diagnostic accuracy

When health care practitioners used the device as a diagnosis support tool, their accuracy in identifying GPP improved significantly (P<.001) across every group.

Group	Accuracy before (%; 95% CI)	Accuracy after (%; 95% CI)	Difference (%; 95% CI)
All health care practitioners	23.70 (18.52-29.17)	46.67 (38.89-55.56)	22.97 (18.52-28.39)
Primary care practitioners	20.20 (14.81-25.40)	44.44 (34.92-55.56)	24.24 (18.50-31.48)
Dermatologists	33.33 (22.22-44.44)	52.78 (38.89-66.67)	19.45 (16.67-22.23)

Diagnostic accuracy for generalized pustular psoriasis before and after using the device, by reader group. The largest relative gain was observed among primary care practitioners.

Conclusions

Use of Legit.Health was associated with a significant improvement in the accuracy of health care practitioners when diagnosing GPP and other dermatologic conditions such as hidradenitis suppurativa. Overall diagnostic accuracy for GPP increased by 22.97%, with primary care practitioners improving by 24.24% and dermatologists by 19.45%.

As a standalone classifier, the device reached a top-1, top-3, and top-5 sensitivity of 0.80, 0.86, and 0.90, respectively, with specificity exceeding 0.90 across all thresholds.

These results highlight that the device could be particularly beneficial in primary care settings, where specialized dermatologic expertise may not be available, helping to reduce misdiagnoses and streamline decision-making.

Product Identification

	Information
Device name	Legit.Health Plus (hereinafter, the device)
Model and type	NA
Version	1.1.0.0
Basic UDI-DI	8437025550LegitCADx6X
Certificate number (if available)	MDR 000000 (Pending)
EMDN code(s)	Z12040192 (General medicine diagnosis and monitoring instruments - Medical device software)
GMDN code	65975
EU MDR 2017/745	Class IIb
EU MDR Classification rule	Rule 11
Novel product (True/False)	TRUE
Novel related clinical procedure (True/False)	TRUE
SRN	ES-MF-000025345