A comprehensive review of AI-powered skincare product recommendation systems: From data collection to user experience

Abstract

Artificial Intelligence (AI) is increasingly infiltrating the field of medicine, particularly in the area of assisted diagnosis. Traditional AI models, limited to single data types, struggle to accurately diagnose skin conditions. Incorporating diverse data sources, such as patient narratives, lab. results, and various skin images, can significantly improve diagnostic capabilities. Large-scale multimodal models, trained on extensive clinical data, can effectively integrate diverse data types to improve diagnostic accuracy. This paper investigates the methodologies, applications, and limitations of unimodal models, while also exploring how multimodal models can amplify precision and reliability. Moreover, amalgamating advanced technologies such as Federated learning and multi-party privacy computing, when integrated with AI, can significantly alleviate privacy risks associated with dermatological data while enabling highly accurate self-diagnosis. Diagnostic systems supported by large-scale pre-trained multimodal models can aid dermatologists in formulating effective diagnostic and treatment roadmaps, ushering in a new frontier in healthcare. Machine learning can transform dermatology by enabling more accurate diagnoses and customized treatment plans. The convergence of vast datasets, including electronic health records, image repositories, and genomic data, coupled with accelerated computing power and affordable storage, has fueled the evolution of sophisticated Machine Learning algorithms capable of replicating acumen resembling dermatological diagnosis and treatment. This paper provides a comprehensive overview of machine learning, encompassing its foundational principles, current applications in dermatology, and potential challenges that may hinder its further development.