Skin LesionsLegitHealth PHNCT07428941
Evaluating an Artificial Intelligence Tool to Help Primary Care Doctors Diagnose Skin Conditions.
An observational study of Skin Lesions and Melanocytic Nevi, 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 primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Skin Lesions, Melanocytic Nevi, Melanoma (Skin Cancer)
- What is being tested?
- AI-based medical device for aided diagnosis in dermatological conditions
- Type of treatment
- Medical device
- How many people
- 9 participants
- Where
- 1 siteSpain
- Study dates
- 4 Jun 2024to 13 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. The participant group includes: * Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms. * 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 primary care physicians regardless of their professional experience.
- High-quality images of patients with different skin conditions.
Who cannot join
- Low-quality images of patients which can not be properly analyzed.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: A Multi-Reader Multi-Case (MRMC) Study Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for Skin Lesions.
This study aims to determine if an artificial intelligence (AI) medical device can help primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to misdiagnoses or unnecessary referrals.
The researchers hypothesized that the information provided by the AI device would increase the true diagnostic accuracy of primary care practitioners for multiple dermatological conditions. To test this, the study followed a prospective, self-controlled design where each participating doctor served as their own comparison.
Read more
During the study, 9 primary care physicians evaluated 30 clinical images representing a variety of skin pathologies. For each image, the doctors followed a two-step process:
- First, they provided a diagnosis based only on the image and the patient's medical history.
- Second, they were shown the AI's analysis-including the top 5 suggested diagnoses and confidence levels-and asked to provide a final diagnosis.
The study also investigated if the AI could help doctors decide whether a patient truly needs a referral to a specialist or if the condition could be handled remotely via teledermatology. The primary question was whether using this AI support would significantly increase the number of correct diagnoses made by primary care doctors and lead to more efficient patient care.
Read the detailed description
This detailed description outlines the clinical methodology, technical framework, and data integrity protocols utilized in the investigation of the Legit Health Plus medical device for skin pathologies in primary care.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional self-controlled study. It utilized a Multi-Reader Multi-Case (MRMC) framework to measure the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each primary care practitioner (PCP) served as their own comparator, providing diagnoses first without and then with device support.
- Sequential Evaluation Workflow: Participants accessed a secure web-based platform to review 30 clinical cases. For each case, doctors provided an initial diagnosis based on an image and medical history, followed by a final diagnosis after reviewing the AI's top 5 suggested ICD-11 categories and confidence levels.
- Clinical Decision Support: The study also evaluated clinician decisions regarding dermatology referrals and the feasibility of remote management (teledermatology) based on AI-provided data, such as malignancy indices.
- Case Distribution: The 30 clinical images represented nine different conditions, including Melanoma, Basal Cell Carcinoma, Psoriasis, and Hidradenitis Suppurativa, all previously confirmed by dermatologists and anatomical pathology.
Quality Assurance and Data Management
To ensure the scientific integrity of the clinical investigation, the following quality and monitoring protocols were implemented:
- Centralized Case Report Forms (CRF): All data were collected via a secure web platform where entries were time-stamped and stored in a central database.
- Data Validation and Checks: Validation rules were applied using computer filters to automatically identify missing values or logical inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect errors.
- Monitoring Plan: An independent clinical monitor oversaw the investigation. Monitoring included remote video or telephone meetings every three months to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155:2020.
- Bias Minimization: Random selection of practitioners helped ensure that outcomes were not influenced by pre-existing group characteristics. Standardized protocols ensured all participants were evaluated under identical conditions.
- Conditions
- Skin Lesions, Melanocytic Nevi, Melanoma (Skin Cancer), Basal Cell Carcinoma of Skin, Urticaria, Pustular Psoriasis
- Being tested
- AI-based medical device for aided diagnosis in dermatological conditions
- Keywords
- Skin conditions, Rare dermatological diseases, Melanoma, Diagnositc accuracy, Referral optimization
- Medical subject headings
- Nevus, Pigmented, Melanoma, Carcinoma, Basal Cell, Urticaria, Keratosis, Actinic, Hidradenitis Suppurativa, Skin Diseases
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 dermatological conditionsDevice
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 |
|---|---|---|
| Primary Care Physicians | 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.
* The group includes 9 primary care physicians (PCPs), allowing for a comparison of PCPs 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 dermatological conditions |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
This measure evaluates the "Top-1" diagnostic accuracy of primary care practitioners (PCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI's top 5 suggestions-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology).
Measured: Day 1
Secondary outcomes (2)
Change in Dermatology Referral Rate Assisted by Artificial Intelligence.
This outcome validates the percentage of cases that practitioners determine should be referred to a dermatology specialist after reviewing the AI-provided information, including malignancy indices and tool recommendations. The goal is to evaluate if the device helps optimize resource allocation by reducing unnecessary referrals
Measured: Day 1
Percentage of Cases Deemed Manageable via Remote Consultation.
This measure assesses the practitioners' evaluation of whether a case can be confirmed and treated remotely through teledermatology based on the AI analysis. A Pearson's chi-squared test is used to analyze the association between referral necessity and remote consultation feasibility.
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 start4 Jun 2024
- Primary completion13 Sep 2024
- Study completion13 Sep 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Skin LesionsStudies 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
- 9 participants (actual)
Sponsor and investigators
- Lead sponsor
- AI Labs Group S.L
- Sponsor type
- Industry (pharma, biotech, devices)
- Collaborators
- Puerta de Hierro University Hospital
- Responsible party
- Sponsor
- Investigators
- Antonio Martorell, PhD, Principal investigator, Hospital Universitari de Manises
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07428941
- Sponsor's protocol ID
- Legit.Health_PH_2024
- Other IDs
- EUPAS1000000644
- Acronym
- LegitHealth PH
- 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 Assessing the Impact of Legit.Health Plus on the Diagnostic Accuracy and Referral Decision-Making of Primary Care Physicians for Skin Lesions.
- ClinicalTrials.gov ID
- NCT07428941
- Acronym / sponsor ID
- LegitHealth PH / Legit.Health_PH_2024
- Conditions
- Skin Lesions, Melanocytic Nevi, Melanoma (Skin Cancer), Basal Cell Carcinoma of Skin, Urticaria, Pustular Psoriasis
- Keywords
- Skin conditions, Rare dermatological diseases, Melanoma, Diagnositc accuracy, Referral optimization
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 9 participants (actual)
- Lead sponsor
- AI Labs Group S.LIndustry (pharma, biotech, devices)
- Collaborators
- Puerta de Hierro University Hospital
- Study start
- 4 Jun 2024 (actual)
- Primary completion
- 13 Sep 2024 (actual)
- Study completion
- 13 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 primary care doctors more accurately identify and manage various skin conditions. Skin issues are a frequent reason for doctor visits, but differences in expertise between general practitioners and specialists can sometimes lead to misdiagnoses or unnecessary referrals.
The researchers hypothesized that the information provided by the AI device would increase the true diagnostic accuracy of primary care practitioners for multiple dermatological conditions. To test this, the study followed a prospective, self-controlled design where each participating doctor served as their own comparison.
During the study, 9 primary care physicians evaluated 30 clinical images representing a variety of skin pathologies. For each image, the doctors followed a two-step process:
- First, they provided a diagnosis based only on the image and the patient's medical history.
- Second, they were shown the AI's analysis-including the top 5 suggested diagnoses and confidence levels-and asked to provide a final diagnosis.
The study also investigated if the AI could help doctors decide whether a patient truly needs a referral to a specialist or if the condition could be handled remotely via teledermatology. The primary question was whether using this AI support would significantly increase the number of correct diagnoses made by primary care doctors and lead to more efficient patient care.
Detailed description
This detailed description outlines the clinical methodology, technical framework, and data integrity protocols utilized in the investigation of the Legit Health Plus medical device for skin pathologies in primary care.
Study Design and Technical Methodology The research was conducted as a prospective observational and cross-sectional self-controlled study. It utilized a Multi-Reader Multi-Case (MRMC) framework to measure the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each primary care practitioner (PCP) served as their own comparator, providing diagnoses first without and then with device support.
- Sequential Evaluation Workflow: Participants accessed a secure web-based platform to review 30 clinical cases. For each case, doctors provided an initial diagnosis based on an image and medical history, followed by a final diagnosis after reviewing the AI's top 5 suggested ICD-11 categories and confidence levels.
- Clinical Decision Support: The study also evaluated clinician decisions regarding dermatology referrals and the feasibility of remote management (teledermatology) based on AI-provided data, such as malignancy indices.
- Case Distribution: The 30 clinical images represented nine different conditions, including Melanoma, Basal Cell Carcinoma, Psoriasis, and Hidradenitis Suppurativa, all previously confirmed by dermatologists and anatomical pathology.
Quality Assurance and Data Management
To ensure the scientific integrity of the clinical investigation, the following quality and monitoring protocols were implemented:
- Centralized Case Report Forms (CRF): All data were collected via a secure web platform where entries were time-stamped and stored in a central database.
- Data Validation and Checks: Validation rules were applied using computer filters to automatically identify missing values or logical inconsistencies. This was supplemented by manual editing and exploratory statistical techniques to detect errors.
- Monitoring Plan: An independent clinical monitor oversaw the investigation. Monitoring included remote video or telephone meetings every three months to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155:2020.
- Bias Minimization: Random selection of practitioners helped ensure that outcomes were not influenced by pre-existing group characteristics. Standardized protocols ensured all participants were evaluated under identical conditions.
Study design
- Observational model
- Other
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Primary Care Physicians | – | Device: AI-based medical device for aided diagnosis in dermatological conditionsThis 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. * The group includes 9 primary care physicians (PCPs), allowing for a comparison of PCPs 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. The participant group includes: * Primary Care Practitioners: General practitioners who often serve as the first point of contact for patients with dermatological symptoms. * 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 (2 listed)
- Board-certified primary care physicians regardless of their professional experience.
- High-quality images of patients with different skin conditions.
Key exclusion criteria (1 listed)
- Low-quality images of patients which can not be properly analyzed.
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 dermatological conditionsThe 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 | Primary Care Physicians |
Primary outcome
Diagnostic Accuracy for Multiple Dermatological Conditions with and without Artificial Intelligence Support.
Time frame: Day 1
This measure evaluates the "Top-1" diagnostic accuracy of primary care practitioners (PCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI's top 5 suggestions-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology).
2 secondary outcomes
Change in Dermatology Referral Rate Assisted by Artificial Intelligence.
Time frame: Day 1
Percentage of Cases Deemed Manageable via Remote Consultation.
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.
- Individual participant dataSharing planned: No
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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 Skin Lesions and Melanocytic Nevi, 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 2 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 Multiple Dermatological Conditions with and without Artificial Intelligence Support. (time frame: Day 1). 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.