Abstract
Advances in artificial intelligence (AI) and biomedical imaging have transformed reproductive medicine, offering new avenues for precision, efficiency, and objectivity in assisted reproductive technology (ART). Traditional embryo selection and ovarian stimulation monitoring rely mainly on subjective interpretation, which is often influenced by inter- and intra-observer variability. In contrast, AI-enhanced models have demonstrated consistent performance, reduced human-dependent discrepancies, and improved reproducibility of clinical decisions. Among emerging technologies, fluorescence lifetime imaging microscopy enables real-time, label-free metabolic assessment of gametes and embryos by quantifying the intrinsic fluorescence lifetimes of nicotinamide adenine dinucleotide phosphate and flavine adenine dinucleotide. These metabolic signatures correlate with developmental competence, providing a non-invasive tool to evaluate embryo quality beyond the morphological criteria. Recent innovations have extended AI and imaging technologies to self-operated reproductive health monitoring. Studies support AI-powered self-assessment of ovarian follicles using smartphone-compatible ultrasound devices and automated follicle segmentation. This development has potential for improving ovarian stimulation tracking, patient engagement, and personalizing treatment protocols in clinical and low-resource settings. The integration of AI, advanced image processing, and metabolic imaging is a promising frontier in reproductive medicine. These tools enhance the precision of embryo and follicle evaluation, while establishing foundations multimodal platforms that combine clinical, morphological, and metabolic data to optimize ART outcomes.