Abstract
Circadian rhythms are intrinsic, approximately 24-hour cycles governing physiological and bio chemical processes, including hormonal secretion, metabolic regulation, and sleep-wake patterns. Disruptions in these rhythms are implicated in a wide range of diseases, from metabolic and cardio vascular disorders to psychiatric and neurodegenerative conditions. Traditional methods for as sessing circadian biology often rely on invasive sampling or require extended monitoring peri ods, posing challenges for clinical implementation. Recent advances in artificial intelligence (AI), particularly in time-series analysis and multimodal data integration, offer new opportunities to predict and model circadian dynamics with greater precision, scalability, and personalization. This review explores the intersection of circadian physiology and AI, focusing on how machine learning and deep learning algorithms can decode and forecast biochemical and hormonal rhythms. We highlight key studies utilizing AI to model patterns of melatonin, cortisol, insulin, and other chronobiological markers in both health and disease. Applications span from sleep medicine and endocrinology to oncology and chronop harmacology, where AI-guided insights may improve diagnosis, disease monitoring, and therapeutic timing. We also discuss the limitations of current models, including issues of data heterogeneity, interpret ability, and ethical concerns related to privacy and continuous monitoring. Finally, we propose fu ture directions for the integration of AI with chronobiological research, emphasizing its potential to revolutionize personalized medicine through real-time, circadian-aware healthcare strategies. By synthesizing current evidence, this review aims to provide a comprehensive foundation for re searchers and clinicians seeking to harness AI in the study and application of circadian physiology.
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