review · Advanced Intelligent Systems
Psoriasis is a chronic skin disorder that remains challenging to diagnose and treat because of its complex mechanisms and differences in how individual patients respond to therapy. Conventional clinical methods struggle to account for these variations. Microfluidic technologies offer useful biomedical tools, yet their design and optimisation are traditionally complicated. Integrating artificial intelligence can address these issues by automating device development and improving experimental workflows. Combined platforms using artificial intelligence and microfluidics with integrated biosensors can accurately identify disease biomarkers, manipulate biological samples, and recreate psoriasis models for laboratory and living systems. These capabilities support real-time monitoring, targeted cell screening, diagnosis, and drug delivery, offering new routes to enhance personalised treatment strategies.
Psoriasis affects millions globally, but standard treatments often fail because the disease behaves differently in each patient. Combining artificial intelligence with microfluidic chips helps researchers recreate realistic disease models and detect vital biomarkers. This provides a clearer path towards testing tailored therapies and delivering more effective, individualised healthcare for chronic skin conditions.
The technology could enable diagnostic devices, cell-screening tools, and drug delivery platforms for dermatological clinics and pharmaceutical developers. However, because this review focuses on overarching system designs, mechanisms, and prospective clinical applications, the technology appears to be at an early research and conceptual stage rather than near-market deployment.
AI-generated from the published abstract. Always read the original work before citing.
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.
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DOI: 10.1002/aisy.202400558
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