A Comprehensive Review of Predictive Precision in Scar Medicine: From Molecular Predictors to Machine Learning Models

Abstract

Scars—including keloids, hypertrophic scars, and acne scars—pose substantial functional and psychosocial burdens that current empirical treatments often address by trial-and-error. Quantitative evidence now supports a precision framework. Validated clinical tools (eg, VSS, POSAS) and imaging modalities (3D photogrammetry; high-frequency ultrasound elastography) provide objective baselines, while emerging AI models deliver measurable gains: an automated scar-type classifier achieved precision 80.7%, recall 71.0%, AUC 0.846 for image-based categorization, and a clinical recurrence model for keloids reported AUC 0.889 with sensitivity 78.7% and specificity 86.8%, enabling earlier risk-stratified interventions and fewer ineffective treatment cycles in model-informed pathways. We synthesize cytokine/fibroblast signatures and genetic predisposition with multimodal (clinical-imaging-molecular) learning, detail validation challenges, and propose actionable safeguards (TRIPOD+AI-aligned reporting, internal-external validation, bias audits, SHAP-based interpretability, and federated learning to preserve privacy and improve generalizability). A pragmatic roadmap—including funding mechanisms, stakeholder roles, and a barrier-solution matrix—aims to accelerate translation toward predictive, preventive, and personalized scar care.