Predictive Biomarkers in Laser Dermatology: Toward Personalized Energy‐Based Treatments

Background Despite major technological advances in laser and energy‐based devices, adverse events such as post‐inflammatory hyperpigmentation (PIH), prolonged erythema, delayed wound healing, and dyschromia remain significant clinical challenges. Current risk assessment largely relies on Fitzpatrick Skin Type classification and clinical judgment, which inadequately capture the biological heterogeneity influencing individual treatment responses. Objective To explore emerging biological biomarkers and predictive technologies that may improve individualized risk stratification, treatment planning, and complication prevention in laser dermatology, thereby supporting the transition toward precision medicine. Methods This perspective article reviews current evidence regarding biological determinants of laser‐induced adverse events, including skin barrier integrity, inflammatory activity, pigmentary response pathways, oxidative stress, microbiome composition, and biological aging. Potential applications of minimally invasive biomarker assessment and artificial intelligence (AI)‐driven predictive modeling are discussed within the context of personalized energy‐based treatments. Results Emerging biomarkers such as transepidermal water loss (TEWL), epidermal cytokine profiles obtained through tape stripping, melanogenic gene expression signatures, oxidative stress markers, microbiome‐associated parameters, and indicators of biological age may provide clinically meaningful information beyond conventional phototype assessment. These markers have the potential to identify patients at increased risk of adverse outcomes, enabling tailored treatment parameters, optimized preconditioning strategies, and enhanced posttreatment monitoring. Furthermore, AI‐based platforms may integrate multimodal biological, imaging, and clinical data to generate individualized safety predictions and support real‐time treatment optimization. Conclusions A biology‐centric approach to laser dermatology may complement existing safety frameworks by incorporating objective molecular and biophysical markers into clinical decision‐making. Although many proposed biomarkers remain investigational, their future integration, particularly when combined with AI‐driven analytics, may improve treatment predictability, reduce complications, and advance equitable, personalized care across diverse patient populations. Further validation studies are needed to establish clinical feasibility, reproducibility, and cost‐effectiveness before routine implementation.