Researchers at Trinity College Dublin have demonstrated a novel non-invasive blood glucose monitoring approach called GlucoRadar, which combines millimeter-wave radar with a highly compact artificial intelligence model. The system operates by emitting 60GHz electromagnetic waves from a sensor positioned less than five centimeters above a target. As these waves interact with glucose molecules, they scatter in proportion to concentration, altering the reflected signal energy and phase characteristics. In laboratory experiments, the team prepared aqueous glucose solutions across 16 concentration levels ranging from 50 to 200 milligrams per deciliter, covering hypoglycemic, normal, and hyperglycemic ranges. After a 2.5-minute signal acquisition period, researchers applied noise reduction, high-pass filtering, and spectral windowing to isolate energy features across the full and five sub-frequency bands. Because the relationship between signal energy and glucose concentration is non-linear, the team deployed a lightweight convolutional neural network consisting of two convolutional layers, one pooling layer, and two fully connected layers. The model requires only approximately 150,000 parameters and occupies 598KB of memory, making it suitable for deployment on standard microcontrollers. Data augmentation through intentional noise injection improved model generalization, yielding a test-set accuracy exceeding 90 percent, with F1 scores surpassing 85 percent for most concentration classes. The research team has open-sourced the system architecture, experimental dataset, and model weights to facilitate further academic development. While the results provide a promising proof-of-concept, significant translational challenges remain. Current testing was conducted exclusively on aqueous solutions; real-world application must account for human skin thickness, hydration, temperature, and perspiration, all of which distort electromagnetic reflections. Additionally, continuous power consumption for wearable integration requires optimization, though the radar chip itself operates below five milliwatts. Unlike established non-invasive optical methods that struggle with signal absorption from skin water and fat, GlucoRadar leverages distinct electromagnetic phase and energy metrics, offering a complementary pathway. No consumer-grade radar glucose monitor has yet received regulatory approval. Nevertheless, the miniaturized design, compatible with chip-level integration, positions the technology as a viable candidate for future embedding in smartwatches and fitness bands, pending further clinical validation and algorithmic refinement.