Progress in paper-based microfluidic diagnostics: toward AI-enhanced, multiplexed detection of pathogens for point-of-care use

Postoperative infections remain a critical concern in surgical care, particularly in resource-limited or field-based operating environments. Paper-based microfluidic diagnostics offer a practical solution for rapid, on-site pathogen detection to support sterile conditions and timely clinical decisions. This review outlines key developments in paper-based testing, from early use of cellulosic substrates and colorimetric assays to more recent integration with smartphone imaging and artificial intelligence (AI). We examine the progression from single-analytic tools to multiplexed systems capable of detecting bacteria, viruses, and biofilms. Highlighted examples include enzyme-linked immunoassays, biofilm-specific staining techniques, and IL-6–based inflammatory markers. We further discuss how convolutional neural networks (CNNs) enhance interpretive accuracy and enable semi-quantitative analysis via mobile platforms. We summarize recent advances in paper-based analytical devices for rapid on-site pathogen detection in resource-limited surgical environments, with a focus on sensitivity, operational simplicity, and readiness for field deployment. In addition, we highlight emerging multiplexing capabilities that enable simultaneous detection of multiple pathogens, representing an important direction for perioperative infection prevention.