AI-Integrated Biosensors for Clinical Practice: Opportunities and Hurdles in Precision Diagnostics

The convergence of artificial intelligence (AI) and advanced biosensing is creating new opportunities to advance clinical diagnostics from reactive treatment toward proactive and precision medicine. This review examines AI-enabled biosensors across four key clinical domains: (1) infectious diseases, focusing on AI-integrated microfluidic point-of-care (POC) testing; (2) cancer, emphasizing AI-assisted liquid biopsy and early cancer detection; (3) neurological disorders, highlighting continuous electrophysiological monitoring; and (4) metabolic diseases, focusing on wearable sensing for personalized care and complication prediction. In addition to these applications, this review discusses key challenges that may limit clinical translation, including data heterogeneity, model overfitting, limited generalizability, sensor variability under real-world conditions, algorithmic interpretability, and regulatory requirements. Explainable AI (XAI), edge computing, and decentralized sensing architectures represent potential approaches to addressing some of these challenges, although their effectiveness depends on the specific application and implementation context. Importantly, promising analytical performance in individual studies does not necessarily establish clinical utility or readiness for routine deployment. Independent validation, evaluation across diverse patient populations and clinical settings, standardized performance reporting, and consideration of implementation costs and regulatory requirements remain important for assessing the clinical applicability of AI-integrated biosensors.