Skin ConditionNCT07429123
Using an Artificial Intelligence Medical Device to Help Primary Care Practitioners Identify and Manage Skin Conditions (Tumor, Inflammatory, and Infectious Diseases) in Adult Patients at Pozuelo and Majadahonda Health Centers
An observational study of Skin Condition and Skin Abnormalities, run by AI Labs Group S.L at 1 site in Spain.
Trial summary
The goal of this observational study is to learn if an artificial intelligence (AI) tool helps primary care practitioners better identify skin conditions. The study focuses on adults with suspected skin pathologies, including tumor, inflammatory, and infectious diseases. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Skin Condition, Skin Abnormalities, Malignancy
- What is being tested?
- Primary care practitioners aided by Legit.Health Plus
- Type of treatment
- Medical device
- How many people
- 131 participants
- Where
- 1 siteSpain
- Study dates
- 24 Jun 2022to 10 Jan 2024
- How long will I take part?
- 1 Year
- 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 is drawn from adult patients presenting with dermatological concerns at two primary care centers in the Madrid region: Centro de Salud de Majadahonda and Centro de Salud de Pozuelo. These participants are residents within the catchment areas of these clinics, with Hospital Universitario Puerta de Hierro Majadahonda serving as their reference hospital for specialized dermatology care. The source population consists of individuals in a real-world clinical setting undergoing preliminary assessment by primary care practitioners (PCPs) for potential referral to specialized dermatology services. This diverse cohort is intended to represent a typical population affected by various skin pathologies, specifically including tumoral (benign and malignant), inflammatory, and infectious conditions. Participants are identified and recruited during routine medical visits whenever a skin-related pathology is suspected.
Who may be able to join
- Tumor pathology:
- Benign:
- Histiocytoma
- Seborrheic keratosis
- Angiomas
- Precancerous:
Show all 25Show fewer
- Actinic keratosis
- Suspected malignancy:
- Basal cell carcinoma
- Squamous cell carcinoma
- Pigmented lesions:
- Melanocytic nevus
- Malignant melanoma
- Inflammatory pathology:
- Psoriasis
- Atopic dermatitis
- Urticaria
- Hidradenitis suppurativa
- Lichen planus
- Infectious pathology:
- Viral warts
- Molluscs
- Herpes simplex
- Patients aged 18 years or older.
- Patients who have signed the informed consent for the study.
Who cannot join
- Patients under 18 years of age.
- Pregnant patients.
- Patients who, in the opinion of the researcher, will not comply with the study procedures.
Trial locations and contacts
1 site in 1 country.
Spain1 site
Puerta de Hierro Majadahonda University Hospital
Majadahonda, Madrid
About this study
Official title: A Project to Enhance Dermatology E-consultations in Primary Care Centers Using Artificial Intelligence Tools.
The goal of this observational study is to learn if an artificial intelligence (AI) tool helps primary care practitioners better identify skin conditions. The study focuses on adults with suspected skin pathologies, including tumor, inflammatory, and infectious diseases.
The main questions it aims to answer are:
Read more
- Does using the AI tool help doctors make more accurate diagnoses for multiple skin conditions?
- Does the tool help doctors better decide which patients need a referral to a dermatologist and which can be managed in primary care?
- Are doctors satisfied with how well the tool works and how easy it is to use in their daily work?
- Can the tool help doctors more accurately differentiate between benign lesions and skin cancer?
Participants will:
- Visit their primary care doctor for a regular skin checkup.
- Have photos taken of their skin condition using a smartphone or a dermatoscope.
- Provide informed consent for their photos and basic health information (such as age and sex) to be analyzed by the AI tool.
- Receive standard care from their doctor, with the tool providing a second opinion to assist in the clinical decision-making process.
Read the detailed description
This prospective, observational study evaluates the clinical utility of an artificial intelligence (AI)-based computational software device designed to support primary care practitioners (PCPs) and dermatologists in managing skin pathologies. The research explores whether the device can enhance diagnostic accuracy and optimize the referral process from primary care to specialized dermatology services.
Study Methodology and Design
The investigation is designed as an analytical study of a clinical case series. Key technical aspects include:
- Investigational Tool: A software-only medical device using computer vision algorithms to analyze images of the epidermis and dermis to provide clinical data for assessment.
- Participant Roles: 15 HCPs (including PCPs and dermatologists) evaluated with a cohort of over 100 patients.
- Procedural Workflow: PCPs captured skin images using smartphones or mobile dermatoscopes, uploaded them to the platform, and provided a diagnosis guided by the AI results.
- Evaluation Baseline: HCPs acted as their own controls, allowing for a comparison of diagnostic performance with and without the AI tool.
Quality Assurance and Registry Procedures
To ensure the integrity of the data collected within this organized system, several quality control measures were implemented:
- Data Validation and Checks: The database utilized consistency rules and logical ranges to control errors during tabulation. Computerized validation filters automatically identified missing values or inconsistencies based on predefined rules.
- Source Data Verification (SDV): A designated independent clinical monitor performed verification of anonymized source documents (e.g., image files and clinical records) against Case Report Forms (CRFs) to ensure accuracy and completeness.
- Monitoring Plan: The research team held quarterly meetings to address data collection issues, while the monitor conducted remote and, if necessary, on-site visits to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155 standards.
- Missing Data Management: Manual editing and exploratory statistical techniques were used to detect and resolve logical errors or inconsistent values before the database was considered closed.
Sample Size and Statistical Principles The study was powered to detect a 10% improvement in diagnostic accuracy.
- Assessment Power: A sample size of 100 patients was determined to provide a 95% confidence level with an 80% power and a margin of error between 9% and 10%.
- Analytical Techniques: Central tendency and variability statistics (mean, SD) were used for quantitative variables, while qualitative variables were analyzed through frequency distributions.
- In addition to parametric tests, the McNemar test was used to analyze the specific impact of AI on HCP diagnostic choices. Statistical significance was set at alpha = 0.05.
For qualitative data, Fisher's exact or Chi-square tests were employed. Statistical significance was set at alpha = 0.05.
Safety and Ethical Standards The study complied with Regulation (EU) 2017/745 (MDR) and ISO 14155:2021. Data protection followed GDPR and Spanish Organic Law 3/2018, utilizing encrypted patient information and alphanumeric identification codes to maintain participant anonymity. All clinical data stored on the device is permanently deleted upon study conclusion.
- Being tested
- Primary care practitioners aided by Legit.Health Plus
- Keywords
- Dermatolgy, Primary care, Skin cancer, Melanoma, Triage, Diagnostic accuracy
- Medical subject headings
- Skin Diseases, Skin Abnormalities, Neoplasms, Melanoma, Skin Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Primary care practitioners aided by Legit.Health PlusDevice
The device is a computer vision software designed to assist healthcare practitioners in assessing skin structures through the analysis of digital images.
Primary care practitioners utilize the device by capturing photographs of affected skin areas with a smartphone or mobile dermatoscope and uploading them to the platform. The software processes images of the epidermis and dermis to quantify visible clinical signs-including intensity, count, and extent-and provides an interpretive distribution of possible International Classification of Diseases (ICD) categories.
Practitioners use the platform's results as a second medical opinion to guide diagnosis, triage, and referral decisions for pathologies including tumoral (benign and malignant), inflammatory, and infectious conditions. The intervention also provides clinicians with access to specific referral criteria, clinical questionnaires, and basic treatment
| Study group | Role | What they receive |
|---|---|---|
| Patiens with skin conditions treated in Primary care | This single-group cohort comprises adult patients presenting with diverse skin pathologies who were evaluated by healthcare professionals (HCPs) using an AI-based clinical decision support tool. The cohort includes individuals suspected of having tumoral (benign or malignant), inflammatory, or infectious conditions. Receives: Device: Primary care practitioners aided by Legit.Health Plus |
What is being measured
The primary outcome is the main question the study is designed to answer.
Referral appropriateness
This metric evaluates the appropriateness of patient referrals from primary care to specialized dermatology services. A referral is classified as "avoidable" or "unnecessary" when both the primary care practitioner and the expert dermatologist agree that the case can be effectively managed within primary care without a specialist consultation. The study's primary target for this metric was a minimum increase in referral adequacy of 15%. This threshold represents the minimum clinically important difference required to demonstrate the device's utility in optimizing clinical workflows and reducing healthcare costs.
Measured: Baseline
Secondary outcomes (2)
Area Under the ROC Curve (AUC) for Malignancy Detection
This measure utilizes the Area Under the ROC Curve (AUC) to evaluate the device's discriminatory performance in differentiating between malignant lesions (including melanoma and basal cell carcinoma) and benign lesions. The AUC provides a comprehensive assessment of the tool's ability to correctly classify skin pathologies across various decision thresholds. The predefined acceptance criterion for this metric was an AUC $\ge$ 80%. This threshold ensures that the device provides clinically meaningful support in identifying high-risk cases that require urgent specialist intervention.
Measured: Baseline
Healthcare Professional Satisfaction (CUS Score)
Satisfaction is evaluated using the Clinical Utility and Satisfaction (CUS) Questionnaire. This validated assessment tool measures practitioners' perspectives on the device's diagnostic support, ease of use, data utility, and overall clinical applicability within their workflow. Results from the questionnaire are quantified either as a percentage of affirmative responses or as a mean score on a 10-point scale. This dual approach allows for both a qualitative understanding of practitioner consensus and a quantitative measure of perceived value.
Measured: 4 months (practitioners completed the questionnaire twice: once at 2 months and again at 4 months after starting the study).
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 start24 Jun 2022
- Primary completion19 Dec 2023
- Study completion10 Jan 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Skin ConditionStudies 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 131 participants (actual)
- Target duration
- 1 Year
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
- Gaston Roustan, PhD, Principal investigator, Puerta de Hierro University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07429123
- Sponsor's protocol ID
- LEGIT.HEALTH_DAO_Derivación_PH
- Status last verified
- Feb 2026
- First submitted
- 10 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
- Undecided
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 Project to Enhance Dermatology E-consultations in Primary Care Centers Using Artificial Intelligence Tools.
- ClinicalTrials.gov ID
- NCT07429123
- Acronym / sponsor ID
- LEGIT.HEALTH_DAO_Derivación_PH
- Keywords
- Dermatolgy, Primary care, Skin cancer, Melanoma, Triage, Diagnostic accuracy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 131 participants (actual)
- Lead sponsor
- AI Labs Group S.LIndustry (pharma, biotech, devices)
- Collaborators
- Puerta de Hierro University Hospital
- Study start
- 24 Jun 2022 (actual)
- Primary completion
- 19 Dec 2023 (actual)
- Study completion
- 10 Jan 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.
The goal of this observational study is to learn if an artificial intelligence (AI) tool helps primary care practitioners better identify skin conditions. The study focuses on adults with suspected skin pathologies, including tumor, inflammatory, and infectious diseases.
The main questions it aims to answer are:
- Does using the AI tool help doctors make more accurate diagnoses for multiple skin conditions?
- Does the tool help doctors better decide which patients need a referral to a dermatologist and which can be managed in primary care?
- Are doctors satisfied with how well the tool works and how easy it is to use in their daily work?
- Can the tool help doctors more accurately differentiate between benign lesions and skin cancer?
Participants will:
- Visit their primary care doctor for a regular skin checkup.
- Have photos taken of their skin condition using a smartphone or a dermatoscope.
- Provide informed consent for their photos and basic health information (such as age and sex) to be analyzed by the AI tool.
- Receive standard care from their doctor, with the tool providing a second opinion to assist in the clinical decision-making process.
Detailed description
This prospective, observational study evaluates the clinical utility of an artificial intelligence (AI)-based computational software device designed to support primary care practitioners (PCPs) and dermatologists in managing skin pathologies. The research explores whether the device can enhance diagnostic accuracy and optimize the referral process from primary care to specialized dermatology services.
Study Methodology and Design
The investigation is designed as an analytical study of a clinical case series. Key technical aspects include:
- Investigational Tool: A software-only medical device using computer vision algorithms to analyze images of the epidermis and dermis to provide clinical data for assessment.
- Participant Roles: 15 HCPs (including PCPs and dermatologists) evaluated with a cohort of over 100 patients.
- Procedural Workflow: PCPs captured skin images using smartphones or mobile dermatoscopes, uploaded them to the platform, and provided a diagnosis guided by the AI results.
- Evaluation Baseline: HCPs acted as their own controls, allowing for a comparison of diagnostic performance with and without the AI tool.
Quality Assurance and Registry Procedures
To ensure the integrity of the data collected within this organized system, several quality control measures were implemented:
- Data Validation and Checks: The database utilized consistency rules and logical ranges to control errors during tabulation. Computerized validation filters automatically identified missing values or inconsistencies based on predefined rules.
- Source Data Verification (SDV): A designated independent clinical monitor performed verification of anonymized source documents (e.g., image files and clinical records) against Case Report Forms (CRFs) to ensure accuracy and completeness.
- Monitoring Plan: The research team held quarterly meetings to address data collection issues, while the monitor conducted remote and, if necessary, on-site visits to ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155 standards.
- Missing Data Management: Manual editing and exploratory statistical techniques were used to detect and resolve logical errors or inconsistent values before the database was considered closed.
Sample Size and Statistical Principles The study was powered to detect a 10% improvement in diagnostic accuracy.
- Assessment Power: A sample size of 100 patients was determined to provide a 95% confidence level with an 80% power and a margin of error between 9% and 10%.
- Analytical Techniques: Central tendency and variability statistics (mean, SD) were used for quantitative variables, while qualitative variables were analyzed through frequency distributions.
- In addition to parametric tests, the McNemar test was used to analyze the specific impact of AI on HCP diagnostic choices. Statistical significance was set at alpha = 0.05.
For qualitative data, Fisher's exact or Chi-square tests were employed. Statistical significance was set at alpha = 0.05.
Safety and Ethical Standards The study complied with Regulation (EU) 2017/745 (MDR) and ISO 14155:2021. Data protection followed GDPR and Spanish Organic Law 3/2018, utilizing encrypted patient information and alphanumeric identification codes to maintain participant anonymity. All clinical data stored on the device is permanently deleted upon study conclusion.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Year
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patiens with skin conditions treated in Primary care | – | Device: Primary care practitioners aided by Legit.Health PlusThis single-group cohort comprises adult patients presenting with diverse skin pathologies who were evaluated by healthcare professionals (HCPs) using an AI-based clinical decision support tool. The cohort includes individuals suspected of having tumoral (benign or malignant), inflammatory, or infectious conditions. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population is drawn from adult patients presenting with dermatological concerns at two primary care centers in the Madrid region: Centro de Salud de Majadahonda and Centro de Salud de Pozuelo. These participants are residents within the catchment areas of these clinics, with Hospital Universitario Puerta de Hierro Majadahonda serving as their reference hospital for specialized dermatology care. The source population consists of individuals in a real-world clinical setting undergoing preliminary assessment by primary care practitioners (PCPs) for potential referral to specialized dermatology services. This diverse cohort is intended to represent a typical population affected by various skin pathologies, specifically including tumoral (benign and malignant), inflammatory, and infectious conditions. Participants are identified and recruited during routine medical visits whenever a skin-related pathology is suspected.
Key inclusion criteria (25 listed)
- Tumor pathology:
- Benign:
- Histiocytoma
- Seborrheic keratosis
- Angiomas
Key exclusion criteria (3 listed)
- Patients under 18 years of age.
- Pregnant patients.
- Patients who, in the opinion of the researcher, will not comply with the study procedures.
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 |
|---|---|---|
| Primary care practitioners aided by Legit.Health PlusThe device is a computer vision software designed to assist healthcare practitioners in assessing skin structures through the analysis of digital images. Primary care practitioners utilize the device by capturing photographs of affected skin areas with a smartphone or mobile dermatoscope and uploading them to the platform. The software processes images of the epidermis and dermis to quantify visible clinical signs-including intensity, count, and extent-and provides an interpretive distribution of possible International Classification of Diseases (ICD) categories. Practitioners use the platform's results as a second medical opinion to guide diagnosis, triage, and referral decisions for pathologies including tumoral (benign and malignant), inflammatory, and infectious conditions. The intervention also provides clinicians with access to specific referral criteria, clinical questionnaires, and basic treatment | Device | Patiens with skin conditions treated in Primary care |
Primary outcome
Referral appropriateness
Time frame: Baseline
This metric evaluates the appropriateness of patient referrals from primary care to specialized dermatology services. A referral is classified as "avoidable" or "unnecessary" when both the primary care practitioner and the expert dermatologist agree that the case can be effectively managed within primary care without a specialist consultation. The study's primary target for this metric was a minimum increase in referral adequacy of 15%. This threshold represents the minimum clinically important difference required to demonstrate the device's utility in optimizing clinical workflows and reducing healthcare costs.
2 secondary outcomes
Area Under the ROC Curve (AUC) for Malignancy Detection
Time frame: Baseline
Healthcare Professional Satisfaction (CUS Score)
Time frame: 4 months (practitioners completed the questionnaire twice: once at 2 months and again at 4 months after starting the study).
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: Undecided
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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.
Gaston Roustan, PhD principal investigator
Puerta de Hierro University Hospital
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 Condition and Skin Abnormalities, 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 25 inclusion and 3 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?
Referral appropriateness (time frame: Baseline). 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.