Advances in Intelligent Recognition and Diagnosis of Skin Scar Images: Concepts, Methods, Challenges, and Future Trends

Fuhua Hu¹Yuan Shao^2,3Junjie Liu^2,4Jialong Liu^2,3Xiaolong Xiao^2,3Kaibing Shi²Yangzong Zheng²Jianfeng Zhang^2,5*Xuelian Wang^1*

• ¹Hangzhou Plastic Surgery Hospital (The Affiliated Hospital of the College of Mathematical Medicine, Zhejiang Normal University), Hangzhou, China
• ²College of Mathematical Medicine, Zhejiang Normal University, Jinhua, China
• ³Zhejiang Normal University School of Computer Science and Technology, Jinhua, China
• ⁴School of mathematical sciences, Zhejiang Normal University, Jinhua, China
• ⁵Puyang Institute of Big Data and Artificial Intelligence, Puyang, China

Skin scars, resulting from the natural healing cascade following cutaneous injury, impose enduring physiological and psychological burdens on patients. This review first summarizes the biological classification of scars, their formation mechanisms, and conventional clinical assessment techniques. We then introduce core concepts of artificial intelligence, contrasting traditional machine learning algorithms with modern deep learning architectures, and review publicly available dermatology datasets. Standardized quantitative evaluation metrics and benchmarking protocols are presented to enable fair comparisons across studies. In the Methods Review section, we employ a systematic literature search strategy. Traditional machine learning methods are classified into unsupervised and supervised approaches. We examine convolutional neural networks (CNNs) as an independent category. We also explore advanced algorithms, including multimodal fusion, attention mechanisms, and self-supervised and generative models. For each category, we outline the technical approach, emphasize performance benefits, and discuss inherent limitations. Throughout, we also highlight key challenges related to data scarcity, domain shifts, and privacy legislation, and propose recommendations to enhance robustness, generalizability, and clinical interpretability. By aligning current capabilities with unmet clinical needs, this review offers a coherent roadmap for future research and the translational deployment of intelligent scar diagnosis systems.