Pustular Psoriasis (PP)LegitHealth BINCT07428915
Evaluating Legit.Health Plus Support for Improving Diagnosis of Generalized Pustular Psoriasis and Other Skin Conditions Among Primary Care Physicians and Dermatologists
An observational study of Pustular Psoriasis (PP) and Generalized Pustular Psoriasis (GPP), run by AI Labs Group S.L at 1 site in Spain.
Trial summary
This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Pustular Psoriasis (PP), Generalized Pustular Psoriasis (GPP), Eczema Atopic Dermatitis
- What is being tested?
- AI-based medical device for aided diagnosis in Dermatology
- Type of treatment
- Medical device
- How many people
- 15 participants
- Where
- 1 siteSpain
- Study dates
- 1 Jun 2024to 15 Sep 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of board-certified healthcare professionals recruited from the clinical fields of general medicine and dermatology. The participant group includes: * Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms. * Specialist Dermatologists: Physicians with advanced expertise in skin pathologies and rare conditions. * Experience Level: The cohort includes practitioners with at least 5 years of clinical experience in their respective specialities. Participants were recruited to engage in a remote, web-based evaluation environment rather than being selected from a single physical hospital or town. The clinical images evaluated as part of the study "cases" were sourced from international public dermatology atlases and existing research databases from the sponsor, representing a diverse global patient population.
Who may be able to join
- Board-certified general practitioners and dermatologists, regardless of their professional experience.
- Good quality images of patients with GPP.
- Good quality images of patients with HS.
- Good quality images of patients with pathologies that can be confused with GPP or HS, leading to a wrong diagnosis.
Who cannot join
- Images of patients with pathologies different from GPP or HS that can be easily identified.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Multi-Reader Multi-Case (MRMC) Study for Assessing the Impact of Legit.Health Plus on the Clinical Assessment of Generalized Pustular Psoriasis and Other Skin Conditions by Healthcare Professionals.
This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists, which can lead to treatment delays for serious conditions like Generalized Pustular Psoriasis (GPP) and Hidradenitis Suppurativa (HS).
The researchers hypothesized that the AI device would enhance the diagnostic accuracy of healthcare professionals for GPP and other dermatological conditions. To test this, the study followed a prospective observational design involving 15 practitioners, including both general practitioners and dermatologists.
Read more
During the study, participants were asked to evaluate 100 clinical images. For each case, they first provided a diagnosis based on the image and patient history alone. They were then shown the AI's analysis-which included the top five suggested diagnoses and confidence levels-and asked if they would like to adjust their initial assessment.
The primary question the study sought to answer was whether the information provided by the AI device could significantly increase the number of correct diagnoses made by these professionals, particularly for rare diseases that are often difficult to identify in a standard clinical setting
Read the detailed description
This investigation is structured as a multi-reader multi-case (MRMC) study. A cohort of 15 healthcare professionals, including 11 primary care physicians and 4 dermatologists, acted as the "readers". These readers evaluated a "case" set of 100 clinical images to assess diagnostic performance both with and without the assistance of the AI device.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional study. It utilized a "physician-as-their-own-control" design to measure the impact of Artificial Intelligence (AI) on diagnostic performance.
- Intervention Workflow: Participants accessed a dedicated web platform where they were presented with 100 clinical cases.
- Evaluation Steps: For each case, practitioners first evaluated a clinical image alongside anamnesis data (e.g., allergies, systemic symptoms) to provide an initial diagnosis.
- AI Support: Subsequently, they were presented with the AI's top 5 suggested diagnoses and associated confidence levels before making a final assessment.
- Image Sourcing: Cases consisted of high-quality images of Generalized Pustular Psoriasis (GPP), Hidradenitis Suppurativa (HS), and various differential "look-alike" conditions such as subcorneal pustular dermatosis and palmoplantar pustulosis.
- Data Sources: These images were curated from public dermatology atlases and internal research databases from the sponsor.
Quality Assurance and Data Management
To ensure the scientific integrity and reliability of the findings, several quality control measures were implemented:
- Data Validation: A validation process of the clinical data was carried out by running computer filters based on validation rules.
- Error Detection: These filters automatically identify missing values or inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect logical errors and inconsistencies.
- Monitoring Plan: The investigation was overseen by a designated clinical monitor appointed by the sponsor.
- Remote Supervision: Monitoring activities included remote meetings every three months to review study progress and ensure ongoing compliance.
- Bias Minimization: Random selection of healthcare professionals (HCPs) was used to ensure that outcomes were not influenced by pre-existing participant characteristics.
- Standardization: The use of standardized procedures ensured that all participants were evaluated in the same way.
Statistical Analysis Plan
The primary goal of the analysis was to quantify Top-1 accuracy, sensitivity, and specificity for both general practitioners and dermatologists.
- Comparative Metrics: The analysis focused on the absolute metric values compared against the state of the art, as well as the percentage of variation attributable specifically to the use of the device.
- Subgroup Analysis: A dedicated analysis was performed for rare diseases, including GPP, Acne Conglobata, and Pemphigus Vulgaris, to assess the device's utility in high-complexity, low-incidence conditions.
Ethical and Confidentiality Framework The study adhered to UNE-EN ISO 14155:2021, the Declaration of Helsinki, and the General Data Protection Regulation (GDPR).
- Anonymization: No data enabling the personal identification of participants or patients was collected.
- Encryption: All information was managed securely in an encrypted format.
- End of Study: All information stored in the device will be totally and permanently deleted at the end of the study.
- Conditions
- Pustular Psoriasis (PP), Generalized Pustular Psoriasis (GPP), Eczema Atopic Dermatitis, Acute Generalized Exanthematous Pustulosis, Acne, Acne Conglobata
- Being tested
- AI-based medical device for aided diagnosis in Dermatology
- Keywords
- Generalized Pustular Psoriasis, Dermatologists, Primary Care Physicians, Rare diseases, Skin conditions
- Medical subject headings
- Psoriasis, Acute Generalized Exanthematous Pustulosis, Acne Vulgaris, Acne Conglobata, Keratosis, Seborrheic, Dermatitis, Seborrheic, Pemphigus, Impetigo
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based medical device for aided diagnosis in DermatologyDevice
The intervention consists of a Computer-Aided Diagnosis (CAD) software-only medical device that utilizes computer vision algorithms to analyze digital images of skin structures. During the study, healthcare professionals use the tool as a diagnostic support system to assist in the evaluation of complex dermatological conditions.
| Study group | Role | What they receive |
|---|---|---|
| Healthcare Professionals (Primary Care Physicians and Dermatologists) | This group is composed of board-certified healthcare professionals (HCPs) who serve as the "readers" in this multi-reader multi-case (MRMC) study. The cohort is uniquely characterized by its internal comparison: each participant acts as their own control.
* Dual Professional Roles: The group includes 11 primary care physicians (PCPs) and 4 dermatologists, allowing for a comparison between generalist and specialist diagnostic baseline performance.
* Interventional Exposure: All participants are evaluated under two distinct conditions: first, providing a diagnosis based solely on clinical images and patient history; second, providing a diagnosis assisted by the AI-based medical device's top 5 suggestions and confidence levels.
* Clinical Expertise: Every member of the cohort has a minimum of 5 years of clinical experience in their respective field. Receives: Device: AI-based medical device for aided diagnosis in Dermatology |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying GPP. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Measured: Day 1
Diagnostic Accuracy for different skin conditions with and without Artificial Intelligence Support
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying the corresponding skin condition. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Measured: Day 1
Secondary outcomes (1)
Diagnostic Accuracy for Rare Dermatological Conditions with and without Artificial Intelligence Support.
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying rare dermatological conditions. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Feb 2026
- Study start1 Jun 2024
- Primary completion15 Sep 2024
- Study completion15 Sep 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pustular Psoriasis (PP)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Prospective
- Enrolment
- 15 participants (actual)
Sponsor and investigators
- Lead sponsor
- AI Labs Group S.L
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Antonio Martorell, PhD, Principal investigator, Hospital Universitari de Manises
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07428915
- Sponsor's protocol ID
- Legit.Health_BI_2024
- Other IDs
- EUPAS1000000910
- Acronym
- LegitHealth BI
- Status last verified
- Feb 2026
- First submitted
- 18 Feb 2026
- Last updated
- 24 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Multi-Reader Multi-Case (MRMC) Study for Assessing the Impact of Legit.Health Plus on the Clinical Assessment of Generalized Pustular Psoriasis and Other Skin Conditions by Healthcare Professionals.
- ClinicalTrials.gov ID
- NCT07428915
- Acronym / sponsor ID
- LegitHealth BI / Legit.Health_BI_2024
- Conditions
- Pustular Psoriasis (PP), Generalized Pustular Psoriasis (GPP), Eczema Atopic Dermatitis, Acute Generalized Exanthematous Pustulosis, Acne, Acne Conglobata
- Keywords
- Generalized Pustular Psoriasis, Dermatologists, Primary Care Physicians, Rare diseases, Skin conditions
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 15 participants (actual)
- Lead sponsor
- AI Labs Group S.LIndustry (pharma, biotech, devices)
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 15 Sep 2024 (actual)
- Study completion
- 15 Sep 2024 (actual)
- First posted
- 24 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This study aims to determine if an artificial intelligence (AI) medical device can help healthcare professionals more accurately diagnose rare and complex skin conditions. Dermatological issues are common in primary care, but there is often a gap in diagnostic accuracy between general practitioners and specialists, which can lead to treatment delays for serious conditions like Generalized Pustular Psoriasis (GPP) and Hidradenitis Suppurativa (HS).
The researchers hypothesized that the AI device would enhance the diagnostic accuracy of healthcare professionals for GPP and other dermatological conditions. To test this, the study followed a prospective observational design involving 15 practitioners, including both general practitioners and dermatologists.
During the study, participants were asked to evaluate 100 clinical images. For each case, they first provided a diagnosis based on the image and patient history alone. They were then shown the AI's analysis-which included the top five suggested diagnoses and confidence levels-and asked if they would like to adjust their initial assessment.
The primary question the study sought to answer was whether the information provided by the AI device could significantly increase the number of correct diagnoses made by these professionals, particularly for rare diseases that are often difficult to identify in a standard clinical setting
Detailed description
This investigation is structured as a multi-reader multi-case (MRMC) study. A cohort of 15 healthcare professionals, including 11 primary care physicians and 4 dermatologists, acted as the "readers". These readers evaluated a "case" set of 100 clinical images to assess diagnostic performance both with and without the assistance of the AI device.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional study. It utilized a "physician-as-their-own-control" design to measure the impact of Artificial Intelligence (AI) on diagnostic performance.
- Intervention Workflow: Participants accessed a dedicated web platform where they were presented with 100 clinical cases.
- Evaluation Steps: For each case, practitioners first evaluated a clinical image alongside anamnesis data (e.g., allergies, systemic symptoms) to provide an initial diagnosis.
- AI Support: Subsequently, they were presented with the AI's top 5 suggested diagnoses and associated confidence levels before making a final assessment.
- Image Sourcing: Cases consisted of high-quality images of Generalized Pustular Psoriasis (GPP), Hidradenitis Suppurativa (HS), and various differential "look-alike" conditions such as subcorneal pustular dermatosis and palmoplantar pustulosis.
- Data Sources: These images were curated from public dermatology atlases and internal research databases from the sponsor.
Quality Assurance and Data Management
To ensure the scientific integrity and reliability of the findings, several quality control measures were implemented:
- Data Validation: A validation process of the clinical data was carried out by running computer filters based on validation rules.
- Error Detection: These filters automatically identify missing values or inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect logical errors and inconsistencies.
- Monitoring Plan: The investigation was overseen by a designated clinical monitor appointed by the sponsor.
- Remote Supervision: Monitoring activities included remote meetings every three months to review study progress and ensure ongoing compliance.
- Bias Minimization: Random selection of healthcare professionals (HCPs) was used to ensure that outcomes were not influenced by pre-existing participant characteristics.
- Standardization: The use of standardized procedures ensured that all participants were evaluated in the same way.
Statistical Analysis Plan
The primary goal of the analysis was to quantify Top-1 accuracy, sensitivity, and specificity for both general practitioners and dermatologists.
- Comparative Metrics: The analysis focused on the absolute metric values compared against the state of the art, as well as the percentage of variation attributable specifically to the use of the device.
- Subgroup Analysis: A dedicated analysis was performed for rare diseases, including GPP, Acne Conglobata, and Pemphigus Vulgaris, to assess the device's utility in high-complexity, low-incidence conditions.
Ethical and Confidentiality Framework The study adhered to UNE-EN ISO 14155:2021, the Declaration of Helsinki, and the General Data Protection Regulation (GDPR).
- Anonymization: No data enabling the personal identification of participants or patients was collected.
- Encryption: All information was managed securely in an encrypted format.
- End of Study: All information stored in the device will be totally and permanently deleted at the end of the study.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Healthcare Professionals (Primary Care Physicians and Dermatologists) | – | Device: AI-based medical device for aided diagnosis in DermatologyThis group is composed of board-certified healthcare professionals (HCPs) who serve as the "readers" in this multi-reader multi-case (MRMC) study. The cohort is uniquely characterized by its internal comparison: each participant acts as their own control. * Dual Professional Roles: The group includes 11 primary care physicians (PCPs) and 4 dermatologists, allowing for a comparison between generalist and specialist diagnostic baseline performance. * Interventional Exposure: All participants are evaluated under two distinct conditions: first, providing a diagnosis based solely on clinical images and patient history; second, providing a diagnosis assisted by the AI-based medical device's top 5 suggestions and confidence levels. * Clinical Expertise: Every member of the cohort has a minimum of 5 years of clinical experience in their respective field. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of board-certified healthcare professionals recruited from the clinical fields of general medicine and dermatology. The participant group includes: * Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms. * Specialist Dermatologists: Physicians with advanced expertise in skin pathologies and rare conditions. * Experience Level: The cohort includes practitioners with at least 5 years of clinical experience in their respective specialities. Participants were recruited to engage in a remote, web-based evaluation environment rather than being selected from a single physical hospital or town. The clinical images evaluated as part of the study "cases" were sourced from international public dermatology atlases and existing research databases from the sponsor, representing a diverse global patient population.
Key inclusion criteria (4 listed)
- Board-certified general practitioners and dermatologists, regardless of their professional experience.
- Good quality images of patients with GPP.
- Good quality images of patients with HS.
- Good quality images of patients with pathologies that can be confused with GPP or HS, leading to a wrong diagnosis.
Key exclusion criteria (1 listed)
- Images of patients with pathologies different from GPP or HS that can be easily identified.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-based medical device for aided diagnosis in DermatologyThe intervention consists of a Computer-Aided Diagnosis (CAD) software-only medical device that utilizes computer vision algorithms to analyze digital images of skin structures. During the study, healthcare professionals use the tool as a diagnostic support system to assist in the evaluation of complex dermatological conditions. | Device | Healthcare Professionals (Primary Care Physicians and Dermatologists) |
Primary outcome
Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support.
Time frame: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying GPP. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
Diagnostic Accuracy for different skin conditions with and without Artificial Intelligence Support
Time frame: Day 1
This measure evaluates the Top-1 diagnostic accuracy of healthcare professionals (HCPs) when identifying the corresponding skin condition. Accuracy is calculated by comparing the clinician's diagnosis (both with and without the device's top 5 suggestions) against the confirmed reference diagnosis for each of the clinical cases.
1 secondary outcome
Diagnostic Accuracy for Rare Dermatological Conditions with and without Artificial Intelligence Support.
Time frame: Day 1
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Feb 2026.
- Study website on RWD catalogues of EMALink provided by the sponsor.
- Individual participant dataSharing planned: No
Related trials for Pustular Psoriasis (PP)
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International Rare And Severe Psoriasis Expert Network
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Antonio Martorell, PhD principal investigator
Hospital Universitari de Manises
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pustular Psoriasis (PP) and Generalized Pustular Psoriasis (GPP), run by AI Labs Group S.L at 1 site in Spain.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Spain. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy for Generalized Pustular Psoriasis (GPP) with and without Artificial Intelligence Support. (time frame: Day 1). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Feb 2026.