Autonomous AI‐Driven Design for Skin Product Formulations

Formulating effective skin products requires navigating complex chemical mixtures,skin biophysical and biochemical properties and manufacturing processes, all under budgetary and time constraints. Controlling dermal permeation, a key driver of efficacy, often presents the primary development bottleneck. Conventional development methods are slow, hampered by low‐throughput, variable test assays (e.g., in vitro release and permeation testing) and limited access to biologically relevant in vitro skin models. This review argues for a shift towards autonomous, assay‐aware formulation design, outlining a closed‐loop framework that unifies intelligent candidate generation, automated experiment selection and robust analysis across a skin‐specific multi‐tiered assay strategy. The foundations of barrier transport and formulation behaviour are first synthesised. Key enabling technologies are then systematically surveyed, including automation technologies (e.g., microfluidic and modular platforms), automated analytics (e.g., chromatographic pipelines, auto‐sampling for diffusion cells) and artificial intelligence (e.g., hybrid mechanistic/data‐driven surrogates and constraint‐aware active learning). Building upon this foundation, a practical framework is discussed that foregrounds cross‐tier calibration between rapid screens and pivotal assay endpoints. Its workflow centres on model generalisation, uncertainty quantification and robust system orchestration. The goal is to provide a credible path towards faster, more reproducible and acceptance criteria‐aligned decisions for skin product formulation efficacy.