DermatitisLegitHealthSANNCT07428954
Using Artificial Intelligence to Help Doctors Identify Different Skin Conditions and Improve Patient Care
An observational study of Dermatitis and Melanoma (Skin Cancer), 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 doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Dermatitis, Melanoma (Skin Cancer), Alopecia
- What is being tested?
- AI-based medical device for aided diagnosis in Dermatology
- Type of treatment
- Medical device
- How many people
- 16 participants
- Where
- 1 siteSpain
- Study dates
- 1 Jun 2024to 10 Oct 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 primary care practitioners and dermatologists, 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 Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions
This study aims to determine if an artificial intelligence (AI) medical device can help doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. While many patients visit primary care for skin issues, general doctors may sometimes have different opinions from specialists, which can lead to delays in getting the right treatment.
The researchers hypothesized that using the AI tool would increase the true diagnostic accuracy of healthcare professionals for multiple skin conditions. To test this, 16 doctors (including 10 general practitioners and 6 dermatologists) evaluated 29 different medical images.
Read more
For each case, the doctors followed a structured process:
- Initial Assessment: Doctors first gave a diagnosis based only on the patient's image and medical history.
- AI Support: Doctors were then shown the AI's top five suggested diagnoses and confidence levels to see if they wished to adjust their final decision.
- Clinical Utility: Doctors also indicated if the patient required a specialist referral and if the case could be handled through a remote (online) consultation.
The primary question the study tried to answer was whether AI support could significantly improve correct diagnoses across 13 different types of skin pathologies-ranging from common rashes to skin cancer-while also making the consultation process faster and more effective for both doctors and patients.
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 and dermatology.
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 evaluate the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each healthcare professional (HCP) served as their own comparator, providing diagnoses first without the use of the device and subsequently with the support of the device on the same set of images.
- Evaluation Workflow: Participants accessed a secure web-based platform to review 29 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 International Classification of Diseases (ICD) categories and confidence levels.
- Clinical Utility Assessment: The study included a specific questionnaire to evaluate the utility of the data, consultation time reductions, and confidence in making remote clinical decisions.
- Pathology Diversity: The dataset included 13 distinct skin conditions, ranging from common ailments like Acne and Dermatitis to malignant conditions such as Melanoma and Basal Cell Carcinoma.
Quality Assurance and Data Management
To ensure the scientific integrity of the investigation, the following quality and monitoring protocols were implemented:
- Data Validation and Checks: A validation process was carried out by running computer filters based on validation rules to automatically identify missing values or inconsistencies. Manual editing and exploratory statistical techniques were also used to complement error detection.
- Monitoring Plan: The investigation was monitored by a designated clinical monitor, independent of the investigational site, to ensure the protection of subject rights and data accuracy. Monitoring included remote video or telephone meetings every 3 months.
- Bias Minimization: HCPs were randomly selected to ensure that outcomes were not influenced by pre-existing participant characteristics. Standardized procedures for conducting the study and measuring outcomes were strictly followed to reduce variability.
Ethical and Confidentiality Framework The study adhered to ISO 14155:2021, the Declaration of Helsinki, and the General Data Protection Regulation (GDPR).
- Anonymization: All clinical images were completely anonymized and sourced from public dermatological atlases, containing no information that would allow the identification of patients.
- Data Security: All data entries were timestamped, encrypted using industry-standard security protocols, and stored in a secure central database.
- Data Retention: Upon completion of the study and drafting of the final report, all information stored in the device platform is scheduled to be permanently deleted.
- Conditions
- Dermatitis, Melanoma (Skin Cancer), Alopecia, Urticaria, Granuloma Annulare, Seborrheic Keratoses
- Being tested
- AI-based medical device for aided diagnosis in Dermatology
- Keywords
- Skin conditions, Dermatologists, Primary Care Physicians, Diagnostic accuracy
- Medical subject headings
- Dermatitis, Melanoma, Alopecia, Urticaria, Granuloma Annulare, Keratosis, Seborrheic, Herpes Simplex, Tinea
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 10 primary care physicians (PCPs) and 6 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 Multiple Dermatological Conditions with and without Artificial Intelligence Support
This measure evaluates the "Top-1" diagnostic accuracy of healthcare professionals (HCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI device's top 5 suggestions and confidence levels-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology)
Measured: Day 1
Secondary outcomes (3)
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, which includes malignancy indices and referral recommendations.
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.
Measured: Day 1
Clinical Utility and Usability Scores for Diagnostic Support.
This outcome assesses the perceived value of the device using a Clinical Utility Questionnaire. It measures average utility of data (on a scale of 0-10, where 10 is most useful), system usability scores, and the impact on consultation time reduction.
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 completion10 Oct 2024
- Study completion10 Oct 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for DermatitisStudies 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
- 16 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
- NCT07428954
- Sponsor's protocol ID
- Legit.Health_SAN_2024
- Other IDs
- EUPAS1000000911
- Acronym
- LegitHealthSAN
- 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 Study for Evaluating the Impact of Legit.Health Plus Device on the Healthcare Practitioners' Assessment of Skin Lesions
- ClinicalTrials.gov ID
- NCT07428954
- Acronym / sponsor ID
- LegitHealthSAN / Legit.Health_SAN_2024
- Conditions
- Dermatitis, Melanoma (Skin Cancer), Alopecia, Urticaria, Granuloma Annulare, Seborrheic Keratoses
- Keywords
- Skin conditions, Dermatologists, Primary Care Physicians, Diagnostic accuracy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 16 participants (actual)
- Lead sponsor
- AI Labs Group S.LIndustry (pharma, biotech, devices)
- Study start
- 1 Jun 2024 (actual)
- Primary completion
- 10 Oct 2024 (actual)
- Study completion
- 10 Oct 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 doctors more accurately identify a wide variety of skin conditions and improve the efficiency of patient consultations. While many patients visit primary care for skin issues, general doctors may sometimes have different opinions from specialists, which can lead to delays in getting the right treatment.
The researchers hypothesized that using the AI tool would increase the true diagnostic accuracy of healthcare professionals for multiple skin conditions. To test this, 16 doctors (including 10 general practitioners and 6 dermatologists) evaluated 29 different medical images.
For each case, the doctors followed a structured process:
- Initial Assessment: Doctors first gave a diagnosis based only on the patient's image and medical history.
- AI Support: Doctors were then shown the AI's top five suggested diagnoses and confidence levels to see if they wished to adjust their final decision.
- Clinical Utility: Doctors also indicated if the patient required a specialist referral and if the case could be handled through a remote (online) consultation.
The primary question the study tried to answer was whether AI support could significantly improve correct diagnoses across 13 different types of skin pathologies-ranging from common rashes to skin cancer-while also making the consultation process faster and more effective for both doctors and patients.
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 and dermatology.
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 evaluate the impact of Computer-Aided Diagnosis (CAD) on clinician performance.
- Self-Controlled Framework: Each healthcare professional (HCP) served as their own comparator, providing diagnoses first without the use of the device and subsequently with the support of the device on the same set of images.
- Evaluation Workflow: Participants accessed a secure web-based platform to review 29 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 International Classification of Diseases (ICD) categories and confidence levels.
- Clinical Utility Assessment: The study included a specific questionnaire to evaluate the utility of the data, consultation time reductions, and confidence in making remote clinical decisions.
- Pathology Diversity: The dataset included 13 distinct skin conditions, ranging from common ailments like Acne and Dermatitis to malignant conditions such as Melanoma and Basal Cell Carcinoma.
Quality Assurance and Data Management
To ensure the scientific integrity of the investigation, the following quality and monitoring protocols were implemented:
- Data Validation and Checks: A validation process was carried out by running computer filters based on validation rules to automatically identify missing values or inconsistencies. Manual editing and exploratory statistical techniques were also used to complement error detection.
- Monitoring Plan: The investigation was monitored by a designated clinical monitor, independent of the investigational site, to ensure the protection of subject rights and data accuracy. Monitoring included remote video or telephone meetings every 3 months.
- Bias Minimization: HCPs were randomly selected to ensure that outcomes were not influenced by pre-existing participant characteristics. Standardized procedures for conducting the study and measuring outcomes were strictly followed to reduce variability.
Ethical and Confidentiality Framework The study adhered to ISO 14155:2021, the Declaration of Helsinki, and the General Data Protection Regulation (GDPR).
- Anonymization: All clinical images were completely anonymized and sourced from public dermatological atlases, containing no information that would allow the identification of patients.
- Data Security: All data entries were timestamped, encrypted using industry-standard security protocols, and stored in a secure central database.
- Data Retention: Upon completion of the study and drafting of the final report, all information stored in the device platform is scheduled to be permanently deleted.
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 10 primary care physicians (PCPs) and 6 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 (2 listed)
- Board-certified primary care practitioners and dermatologists, 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 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 Multiple Dermatological Conditions with and without Artificial Intelligence Support
Time frame: Day 1
This measure evaluates the "Top-1" diagnostic accuracy of healthcare professionals (HCPs). Accuracy is determined by comparing the clinician's identified diagnosis-both before and after receiving the AI device's top 5 suggestions and confidence levels-against a confirmed reference standard (confirmed by dermatologists or anatomical pathology)
3 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
Clinical Utility and Usability Scores for Diagnostic 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
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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 Dermatitis and Melanoma (Skin Cancer), 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.