Clinical Validation of AI-Based System for Continuous Remote Monitoring of Patient Severity - Experts' Opinion

Overview

The goal of this observational study is to learn if an artificial intelligence (AI) tool, called Legit Health Plus, can track the severity of chronic skin conditions from a distance. The study included 160 participants who have various skin issues, such as acne, psoriasis, or atopic dermatitis (a type of eczema). The main questions it aims to answer are: * Can this computer-aided diagnosis (CAD) system reliably track how a person's skin condition changes over time? * Does using the tool lead to fewer in-person doctor visits? * Are participants satisfied with using the tool at home? Because this study focuses on evaluating the tool in a real-world setting, researchers did not use a comparison group. What Participants Will Do Participants will use a smartphone app for 6 months to help their doctors monitor their skin. They will: * Take photos of their skin with their own smartphones and send them to their doctor through the app. * Answer survey questions about their symptoms and how the condition affects their daily life. * Complete surveys every two months to share if they are satisfied with the tool and if it is easy to use. How Utility and Usability are Assessed After the study, researchers and doctors will assess if the tool is practical and helpful for medical practice using several methods: * Clinical Utility Questionnaire (CUS): Doctors use this to rate how well the AI tool tracks disease progression and helps them prioritize which participants need care first. * Time Tracking: Researchers check if the tool lowers the time doctors spend on visits, allowing them to manage their workload more efficiently. * System Usability Scale (SUS): Both doctors and participants use this to rate if the app is easy to navigate, simple to learn, and not too complex. * Data Utility Questionnaire (DUQ): Doctors judge if the information collected by the app is useful for their regular practice and remote consultations.

Study Overview and Rationale The investigation was designed in response to the COVID-19 pandemic's disruption of dermatology care, which highlighted the need for efficient, remote tools to monitor chronic conditions like psoriasis, eczema, and acne. Current monitoring often relies on subjective human assessment; this study evaluates whether an Artificial Intelligence (AI) tool can provide more objective, continuous data from a participant's home to support clinical decision-making. Objectives and Hypothesis Primary Objective: To validate the device's ability to reliably track the progression of chronic dermatological conditions. Success is measured by achieving a specific score on the Clinical Utility Questionnaire (CUS). Secondary Objectives: To confirm high participant satisfaction with remote use, demonstrate a potential reduction in face-to-face consultations, and establish the device as a trustworthy monitoring system. Hypothesis: The device can perform objective, continuous remote monitoring, increasing participant empowerment and reducing the need for frequent hospital visits. Research Design and Methodology This is a prospective, observational, and analytical study involving a single group of participants. Target Population: 160 adult participants (over age 18) diagnosed with chronic skin pathologies, including Psoriasis, Urticaria, Acne, Atopic Dermatitis, and others. Duration: The total study duration was 18 months, with each participant followed for a 6-month period. Participant Tasks: Initial Visit: Participants are recruited, provide informed consent, and receive a study code. They capture their first photographs under medical supervision. Remote Monitoring: At home, participants use their own smartphones to capture and transmit photos of affected areas at intervals determined by their specialist. Questionnaires: Participants regularly complete symptoms and quality of life surveys (DLQI) within the app. Data Quality and Statistical Analysis To ensure the integrity of the findings, the study implemented rigorous quality assurance and statistical protocols. Quality Assurance and Monitoring Site Monitoring: A designated independent monitor conducted reviews every 3 months (or every 5 participants) to verify data accuracy and ensure compliance with the Clinical Investigation Plan (CIP) and ISO 14155:2020 standards. Data Validation: Computer filters automatically identify missing values or inconsistencies, while manual editing is used to detect logical errors. Source Data Verification (SDV): The sponsor verified anonymized source documents, such as images and clinical records, against the electronic case report forms (CRFs). Statistical Principles * Primary Analysis: A one-sample Student's t-test was used to compare the mean CUS scores against the target threshold of 8.0/10. * Secondary Analysis: Qualitative variables (like "yes/no" survey responses) are analyzed using frequency distributions, while quantitative data are summarized using means, medians, and standard deviations. * Sample Size: The sample of 6 dermatologists and 160 participants was chosen to minimize observer variability while providing enough cases for a meaningful assessment. Ethical and Safety Considerations The study adhered to the Declaration of Helsinki and Good Clinical Practice (GCP) guidelines. Data Protection: All participants were assigned alphanumeric codes to ensure anonymity. All data processing complied with GDPR and Spanish data protection laws. Safety Monitoring: The study tracked Adverse Events (AE) and Serious Adverse Events (SAE). In this investigation, no adverse events or product reactions were observed. Device Licensing: The manufacturer (AI Labs Group S.L.) provided the device free of charge for the study, but had no access to individual participant accounts or medical information.