Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

Abstract

Microfluidics has evolved into a transformative technology with far‐reaching applications in biomedical research. However, designing and optimizing custom microfluidic systems remains challenging because of their inherent complexities. Integrating artificial intelligence (AI) with microfluidics promises to overcome these barriers by leveraging AI algorithms to automate device design, streamline experimentation, and enhance diagnostic and therapeutic outcomes. Psoriasis is an incurable dermatological condition that is difficult to diagnose and treat owing to its complex pathogenesis. Traditional diagnostic and therapeutic approaches are often ineffective and fail to address individual variabilities in disease progression and treatment responses. However, AI‐coupled microfluidic platforms have the potential to revolutionize psoriasis research and clinical applications with expansive dermatological applications. AI‐driven microfluidic chips with embedded biosensors have the potential to precisely detect biomarkers (BMs), manipulate biological samples, and mimic psoriasis‐like in vivo and in vitro models, thereby allowing real‐time monitoring and optimized therapeutic testing. This review examines the transformative potential of AI and AI‐powered microfluidic platforms for advancing psoriasis research. It examines the design and mechanisms of AI‐coupled microfluidic platforms for cell screening, disease diagnosis, and drug delivery. It highlights recent advances, clinical applications, challenges, future perspectives, and ethical considerations to enhance personalized care and patient outcomes.