Artificial Intelligence in Reproductive Histology and Embryology: A Clinical Informatics Framework for Diagnosis, Prognosis, and Decision Support

Artificial intelligence (AI) is increasingly being integrated into reproductive medicine, yet the translation of AI-based approaches from laboratory research to clinical practice remains incomplete. This review examines the convergence of AI with reproductive histology and embryology, focusing on applications in tissue image analysis, endometrial receptivity assessment, embryo and gamete evaluation, diagnosis, prognosis, and personalized decision-making. AI and deep-learning approaches can support automated image interpretation, embryo assessment, morphokinetic analysis, and integration of heterogeneous reproductive data, while emerging multimodal systems may further expand clinical decision support. However, technical performance alone does not establish clinical utility. Current evidence is limited by retrospective and single-centre studies, heterogeneous datasets and outcomes, incomplete external validation, limited calibration and decision-analytic evaluation, and persistent challenges in interpretability, bias, privacy, and generalisability. A clinical translation framework is proposed that links multimodal reproductive data and AI integration with clinical validation, explainability, clinical utility, and human oversight to support diagnosis, prognosis, and personalized decision-making. Future progress will require prospective multicentre validation, standardized datasets, robust governance, and patient-centred outcome assessment. AI should therefore be viewed as an augmentative component of reproductive care, complementing rather than replacing clinical and embryological expertise, with its value ultimately determined by demonstrated clinical utility and patient-centred outcomes.