Background: Ablative fractional photothermolysis serves as an excellent in vivo model for studying wound healing. The advent of non-invasive imaging devices, such as line-field confocal optical coherence tomography (LC-OCT), enhances this model by enabling detailed monitoring of skin wound healing over time. Additionally, artificial intelligence (AI)-based algorithms are revolutionizing the evaluation of clinical images by providing detailed analyses that are unfeasible manually. Objectives: This study aims to assess the value of combining LC-OCT and AI for evaluating the acute wound healing process in the skin. Methods: The forearms of participating volunteers were ablated with a CO₂ laser in a fractional pattern (7.5 mJ/MTZ) (ClinicalTrials.gov identifier: NCT05614557). To induce observable wound healing differences, two different approved silicone-based formulations were randomly assigned to two test sites, with a third site left untreated. In vivo LC-OCT images were obtained at predefined intervals post-laser treatment, ranging from 1 to 7 days. These images were further analysed using AI algorithms. Results: LC-OCT visualization allows for the characterization of the structural reorganization of the skin during wound healing. The additional integration of AI algorithms significantly enhances the evaluation of the efficacy of wound care interventions by providing a deeper understanding of how these interventions improve wound healing. This is particularly valuable for primary care providers and dermatologists, as AI algorithms have proven useful in observing, characterizing and understanding keratinocyte behaviour. Conclusions: The combination of AI and high-resolution imaging represents a promising tool for better understanding wound healing, evaluating the efficacy of current wound care interventions and analysing keratinocyte behaviour in detail during the wound healing process. Gov identifier: NCT05614557.