J Drugs Dermatol. 2026 Nov 1;25(11):1014-1018. doi: 10.36849/JDD.9982.
ABSTRACT
BACKGROUND: A practical application of artificial intelligence (AI) in healthcare is to generate artificial datasets that capture the characteristics of a real source dataset without exposing protected health information. This approach is particularly relevant to research settings where recruitment and long-term follow-up are barriers to generating robust clinical evidence.
OBJECTIVE: To describe the protocol of a study incorporating augmented patient datasets to assess the efficacy of a dermo-cosmetic product (Cleanance Comedomed+®) in maintaining acne remission after discontinuation of oral isotretinoin.
METHODS: In a randomized, placebo-controlled, blinded clinical trial, patients will receive the dermo-cosmetic product or placebo for up to 12 months. Study outcomes are the number of observed relapses and time to first relapse. To overcome recruitment constraints and reach the 400 patients required to ensure sufficient precision for a time-dependent event criterion, a cohort augmentation strategy using artificial data is implemented. The technology relies on generative models of the Riemannian Hamiltonian Variational Autoencoder type, which learn a compact and geometrically structured latent representation of real clinical data. Generation of artificial patients will occur iteratively, and the initial dataset will be progressively enriched as new real inclusions become available. Reliability of generated data will be assessed and validated through human oversight using quantitative and qualitative metrics covering 4 dimensions: (1) statistical fidelity; (2) diversity and utility; (3) confidentiality; and (4) clinical plausibility.
DISCUSSION AND CONCLUSION: Structured integration of artificial data into this clinical trial illustrates the potential of generative AI as a methodological tool complementing conventional approaches in clinical research.  .
PMID:42976835 | DOI:10.36849/JDD.9982
