Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis

Recently, the integration of smartphone-based platforms into biomedical sensing has provided portable, low-cost, and scalable alternatives to conventional laboratory diagnostics. According to the advances in mobile imaging, embedded sensors, microfluidics, and wireless connectivity, these systems enable the real-time detection and quantitative determination of a wide range of biological and chemical targets, including nucleic acids, proteins, cells, and pathogens, directly at the point of care. In this review, we summarize the existing smartphone-integrated biosensing technologies, with a focus on studies published since 2020. We examine various sensing modalities, including optical, e.g., brightfield, fluorescence, dark-field, electrochemical, and hybrid systems, as well as enabling components such as CMOS imagers, illumination sources, and microfluidic chips. We also highlight the integration of artificial intelligence (AI) and deep learning, which enhance diagnostic accuracy through image enhancement, modality translation, and automated quantification. Despite the rapid progress in this field, challenges still exist in terms of hardware variability, standardization, and clinical validation. In the future, the development of modular attachments, such as open-source hardware, and cloud-connected analytics will be a key to scaling smartphone-based diagnostics for decentralized healthcare, environmental monitoring, and global biosensing applications. This review also provides important biomedical applications of smartphone-based biosensing platforms, including the detection of ocular, metabolic, and urological diseases as well as various cancers, infectious agents, and exosome-derived biomarkers.