Artificial Intelligence-Driven Reproductive Bioengineering: Integrating Fertility Diagnostics, Organ-on-Chip Systems, Cryobiology and Epigenetic Safety for Precision Reproductive Medicine

Vollständiger Abstract

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Traditional assisted reproductive technologies (ART) remain constrained by subjective, descriptive diagnostics and empirical, one-size-fits-all preservation strategies that expose gametes to nonphysiological stressors, risking disruptions to cellular homeostasis and epigenetic programming. This review explores the technological convergence of artificial intelligence (AI), reproductive organ-on-chip bioengineering, multiomics, and translational cryobiology and proposes an integrated, systems-level paradigm for next-generation precision reproductive medicine. By evaluating the clinical readiness, mechanistic insights, and translational trajectories of these emerging platforms, we show how AI architectures transition fertility diagnostics from descriptive metrics to predictive computational phenotyping by integrating high-dimensional imaging, multiomics, and sperm functional datasets. Concurrently, microphysiological platforms—such as testis-, ovary-, and endometrium-on-a-chip systems—recapitulate complex multicellular architecture and endocrine dynamics. When embedded with miniaturized biosensors and machine learning loops, these “smart” closed-loop microfluidic devices enable real-time biological monitoring and adaptive culture regulation. Furthermore, integrating AI analytics into cryobiology optimizes nonlinear thermodynamic variables, shifting the field from basic postthaw morphologic survival toward safeguarding macromolecular fidelity, mitochondrial competence, and long-term epigenetic safety across lifespans and generations. Ultimately, this computational–bioengineering roadmap transitions reproductive healthcare from a reactive discipline into a predictive, personalized, and adaptive framework. Overcoming persistent challenges in biological complexity, data interoperability, and multicenter clinical validation will lead to the establishment of safe, scalable, and ethically governed healthcare infrastructures capable of protecting developmental integrity.