Haut.AI launches AI Body Analysis & summer skin survey –>
A traditional skin efficacy study enrolls 30 to 35 participants and takes eight to sixteen weeks to set up. The results then shift depending on who did the grading. For R&D teams whose claims have to hold up across markets, that combination of small panels, long lead times, and scores that don't reproduce is the real limit on what a research program can learn.
The limiting factor has rarely been scientific expertise. It has been measurement.
Haut.AI's Clinical Studies Software is a validated platform for running remote and hybrid clinical skin research at scale, applying standardized image capture and AI-based measurement to quantify 48 validated biomarkers.
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For decades, clinical skin assessment has depended on controlled testing environments, costly instrumentation, limited participant pools, and manual grading by trained experts. A traditional efficacy study typically enrolls 30 to 35 participants.
These methods remain industry standards, and they are rigorous. But they carry structural costs: they restrict study scale, raise per-participant spend, limit geographic reach, and produce results that vary between graders, which makes findings difficult to reproduce. A study that cannot be reproduced across sites is difficult to use as the basis for a claim across markets.
The same skin can receive different scores from different experts, or from the same expert on different days. That variation enters the dataset as random noise, and noise can obscure a real (or lack of) product effect.
The problem is not that expert graders lack skill. It is that manual grading introduces variance that has nothing to do with the product being tested.
An AI model applies one standard to every image, in every location, at every timepoint. Its measurements are consistent by construction. That consistency has a practical consequence: studies can run across multiple sites and regions without sending trained graders to each one, and results stay comparable across locations, timepoints, and capture settings. Reducing the noise floor makes a genuine effect easier to see.
This is also what makes larger studies workable. Where a traditional study enrolls dozens of participants, the platform is designed to support cohorts of 100 to 7,000, with at-home images delivering grading quality comparable to technician-captured photographs.
"Clinical research in beauty and skincare has reached an inflection point. The industry has incredible expertise in clinical science, but the tools used to collect and analyze data have remained largely unchanged for years. Our goal is not to replace clinical studies. It's to enhance them by making skin measurable at scale. R&D teams that can measure continuously across larger populations don't just do better science; they make faster decisions." Anastasia Georgievskaya, CEO and Co-Founder, Haut.AI
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Haut.AI's skin measurement technology was evaluated in a study conducted at Institut d'Expertise Clinique (I.E.C.) in France, compared against a consensus panel of expert graders. The underlying AI models were developed in collaboration with dermatologists.
The validation demonstrated ICC 0.97 to 0.98 repeatability across five facial skin endpoints, a level of consistency considered excellent by clinical standards. The intraclass correlation coefficient is the standard statistic for repeatability: it expresses how closely repeated measurements of the same subject agree with one another. Those values hold across all three capture settings the platform supports: a clinical camera, a smartphone at a research site, and an at-home selfie. Validation was conducted across participants aged 20 to 70 with balanced gender representation.
For structural aging signs and pigmentation, the AI's grades correlate strongly with a dermatology expert consensus panel. That agreement is maintained whether the image is captured on medical-grade equipment or by the participant on their own phone at home.
The second half of that sentence is the one worth pausing on. Capture-device independence is what turns a measurement method into a distributed study design.
Standardized image-derived measurement also turns visible skin traits into quantitative variables that can sit in the same dataset as molecular markers.
"Visible skin aging has always been harder to quantify with the same rigor we apply to molecular aging markers. Haut.AI's Clinical Studies Software gave us standardized, image-derived measures of facial aging traits that we could pair directly with our DNA methylation data. That combination let us treat visible aging as a quantitative trait alongside our biological measurements, instead of relying on subjective grading." Varun Dwaraka, PhD, FRSB, Director of Research and Principal Investigator, TruDiagnostic
The Clinical Studies Software digitizes the full clinical workflow through five integrated layers. Where traditional studies take eight to sixteen weeks to set up, the platform moves from setup to study start in two to three days.
Each layer removes a handoff. Collectively, they remove the reason study setup takes months.
In a traditional efficacy study, most of the cost sits in work that has to be repeated at every visit and every stage. The Clinical Studies Software automates that repeated work through AI measurement and remote self-submission.
Four functions the platform takes on:
These four functions carry no added cost. The consequence is a different budget shape: spend scales with recruitment, not with headcount or visits. And because remote capture removes per-participant site visit costs, teams can run larger cohorts for comparable total spend. Across a typical program, the platform delivers 3x average return on investment, based on an average of six studies per year.
That changes the question an R&D team asks. The constraint moves from what a team can afford to measure toward what it actually wants to know.
The Clinical Studies Software builds on technology already deployed by some of the world's largest beauty, skincare, and ingredient companies, from Fortune 500 personal-care manufacturers to global active-ingredient houses and leading retail beauty brands. Deployments include:
The platform automates measurement and study operations. Ethics approval, compliance, and participant recruitment remain with the research partner, and the platform is designed to sit alongside an existing CRO rather than displace one: the CRO retains ethics, IRB, protocol adherence, and recruitment, while Haut.AI provides the measurement and analytics layer.
Privacy is built into the design rather than added at the end. All facial imagery used in analysis is anonymized via Haut.AI's patented Skin Atlas technology, and personal and recruitment data remains with the research partner. The separation is deliberate: the party that holds identity is not the party that performs the measurement.
Three shifts follow from measurement that is consistent, remote, and inexpensive to repeat.
First, sample size stops being primarily a budget decision. When grading carries no marginal cost, a cohort of several hundred becomes a design choice rather than a line item.
Second, assessment can become longitudinal rather than episodic. Participants submit images at whatever interval the protocol requires, which makes it possible to observe how an effect develops over time rather than only whether it exists at endpoint.
Third, geography stops constraining the study population. Standardized capture and a single measurement standard mean a cohort can span markets and skin tones without the reproducibility penalty that multi-site manual grading normally introduces.
None of this replaces clinical science. It gives clinical science a wider aperture. For teams whose claims have to hold up across markets, that is the difference between evidence that travels and evidence that has to be repeated.
What is clinical studies software?
Clinical studies software digitizes the design, data capture, measurement, analysis, and reporting stages of a clinical study within a single platform. Haut.AI's Clinical Studies Software is purpose-built for skin research: it standardizes image capture and applies AI-based measurement to quantify skin biomarkers across remote, in-clinic, and hybrid study designs.
How many biomarkers does it measure?
48 validated biomarkers in total, spanning acne and breakouts, lines and wrinkles, pigmentation and tone, pores and texture, and aging and structural signs. Every biomarker maps to a specific measurable parameter, so endpoints can be selected to match the claim a study is designed to support.
Can at-home photographs be used in clinical skin research?
Yes, when capture is standardized. In Haut.AI's validation, at-home images delivered grading quality comparable to technician-captured photographs, and agreement with a dermatology expert consensus panel on structural aging signs and pigmentation was maintained whether the image came from medical-grade clinic equipment or the participant's own phone.
How does AI measurement address grader variability?
Expert graders can score the same skin differently, and the same grader can score differently on different days. An AI model applies one standard to every image at every timepoint and location, so measurements are consistent by construction and remain comparable across sites.
How repeatable is the measurement?
A study conducted at Institut d'Expertise Clinique (I.E.C.) demonstrated ICC 0.97 to 0.98 repeatability across five facial skin endpoints, compared against a consensus panel of expert graders, a level of consistency considered excellent by clinical standards.
How large can a study be?
Traditional skin efficacy studies typically enroll 30 to 35 participants. The platform supports cohorts of 100 to 7,000; existing deployments include a single consumer study that screened more than 7,000 participants.
Does this replace traditional clinical studies?
No. The platform automates measurement and repeated study operations. Study design, ethics approval, compliance, and recruitment remain with the research partner, and the intent is to widen the scale at which clinical research can be conducted rather than to substitute for it.
Can it work alongside our existing CRO?
Yes. Haut.AI operates as a technology partner within an existing study structure. The CRO retains responsibility for ethics, IRB, protocol adherence, and subject recruitment; Haut.AI provides the measurement and analytics layer.
Planning a skin efficacy study, or reconsidering how you measure one? Explore the Clinical Studies Software, or read our explainer on clinical versus consumer applications of AI skin analysis.
