Traditional clinical skin research has long been constrained by the limitations of controlled environments, costly manual grading, and small sample sizes—usually capping at 35 participants. This reliance on human experts introduces subjective variability, where the same skin can receive inconsistent scores depending on the grader or the day. The new Haut.AI platform addresses this by applying a standardized AI model to every image, ensuring consistent data collection across multiple global sites without requiring researchers to be physically present.
In section Releases
Haut.AI Debuts Platform to Automate Large-Scale Clinical Skin Studies
Haut.AI has launched a clinical studies software designed to transition dermatological research from manual, site-based assessment to automated, remote environments. By using AI to quantify 48 skin biomarkers, the platform aims to slash study setup times by over 90% while enabling cohorts involving thousands of participants.
Scaling Dermatology Research
The platform integrates image capture and analysis to track endpoints like wrinkles, pigmentation, and acne. By replacing manual workflows with remote, participant-led image submission, the software allows researchers to conduct studies at scale. According to CEO Anastasia Georgievskaya, the goal is to enhance the rigor of clinical science by making skin measurable across larger populations, which facilitates faster R&D decision-making. Validation studies conducted with the Institut d'Expertise Clinique demonstrated repeatability scores of 0.97 to 0.98, indicating a level of consistency comparable to expert consensus panels. The system is already in use by major industry players, including Fortune 500 manufacturers and global ingredient suppliers, to execute longitudinal research programs and large-scale consumer efficacy screenings.
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