Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

TL;DR: Researchers developed an unsupervised learning-assisted nano-lens holography platform for ultrasensitive, amplification-free detection of viable bacteria, achieving 99 nm sensitivity and 38 CFU/mL-1 detection limit, with potential applications in food safety and clinical diagnosis.

TL;DR: Researchers develop a smartphone-based colorimetry system that overcomes ambient imaging biases and manufacturer variability, enabling real-time imaging and clinical diagnostics, including skin analysis for cyanosis and oxygen concentration measurement, with skin tone adaptation.

TL;DR: Researchers introduce Quantella, a smartphone-based platform for comprehensive cell analysis, addressing limitations of conventional systems with low-cost optics, adaptive image processing, and cloud-connected mobile app, achieving high-throughput, reproducible results with deviations under 5% from flow cytometry.

Abstract: Noninvasive diagnostic approaches are essential for early detection, patient compliance, and reduction of healthcare burden, yet they often face limitations in sensitivity, specificity, and timely interpretation. Artificial intelligence (AI) and machine learning (ML) address these gaps by uncovering complex patterns in diverse data streams and, in some instances, transforming diagnostics from isolated, ad hoc assessments into continuous, real-time monitoring. This review explores the integration of AI/ML across key noninvasive platforms, including medical imaging, wearable sensors, breath analysis, biofluid-based diagnostics (saliva, sweat, urine), and optical sensing methods. It synthesizes the current state of these technologies while highlighting emerging directions such as federated learning, explainable AI, digital twins, and the incorporation of nanosensors. Alongside technological advances, this review critically discusses barriers to adoption, including data privacy, algorithmic fairness, regulatory hurdles, and system integration challenges. By providing a comprehensive, modality-wise perspective, this article aims to guide researchers, clinicians, healthcare professionals, and policymakers in understanding both the promise and the practical limitations of AI-assisted noninvasive diagnostics. Ultimately, it offers a roadmap for translating innovation into scalable, cost-effective, and patient-centered solutions that can broaden healthcare access and improve outcomes globally.

TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.

TL;DR: This work bridges the technological gap between signal transduction, conditioning, processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.

TL;DR: A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.

TL;DR: Relation Network (RN) as mentioned in this paper learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting.