Dermatology 2026

Artificial intelligence is creating new opportunities to discover biological targets, design candidate molecules and investigate interventions relevant to aesthetic medicine and rejuvenation. This presentation examines how these capabilities could shape future advances, using rentosertib as a concrete example of translation from computational discovery to human research.

Through a narrative review of published studies, we examine three connected stages: AI-assisted target identification, generative molecular design and biomarker-based assessment. Rentosertib, an investigational TNIK inhibitor developed for idiopathic pulmonary fibrosis, illustrates the first two stages. A September 2026 exploratory analysis of 42 trial participants reported lower predicted biological age across six proteomic clocks, while acknowledging that disease-specific and aging-related effects could not be fully separated.

Building on this case, we propose a discovery framework for aesthetic medicine: define an unmet clinical need, integrate relevant biological and clinical data, prioritize hypotheses, validate candidates experimentally and evaluate meaningful outcomes prospectively. Potential research directions include skin aging, tissue remodeling and individualized response prediction. These are proposed applications, not demonstrated benefits of rentosertib. From a computer engineering perspective, the presentation discusses data quality, model generalizability, interdisciplinary collaboration and the distinction between predictive accuracy and clinical benefit. No original patient data are presented. The central argument is that AI can expand the range of testable ideas and improve how discoveries are pursued. Progress in rejuvenation will depend on connecting those computational capabilities with reproducible experiments, appropriate safety assessment and clinically meaningful evidence.

Albert Orta is a computer engineer and CEO of AELVYA, a company focused on aesthetic technology and clinic supply. His professional background includes technology leadership, business consulting and the development of artificial intelligence and automation solutions. He is interested in how collaboration between engineers, researchers and healthcare professionals can turn emerging technologies into practical advances. His contribution to this congress examines AI-enabled discovery and its potential relevance to aesthetic medicine and rejuvenation from an engineering and business perspective.