Explainable AI in Male Infertility: A Clinical Roadmap for Shared Decision‐Making

Artificial intelligence (AI) is increasingly entering male infertility care through semen image analysis, sperm-selection support, prediction of sperm retrieval, and decision-support tools for assisted reproduction. Yet clinical value will depend less on algorithmic novelty than on explainability, governance, and safe integration into existing andrology practice. This Perspective proposes a pragmatic roadmap for explainable AI in male infertility, moving from laboratory quality support to externally validated prediction tools and shared decision-making. Explainability methods such as feature importance, SHAP, LIME, and saliency maps may help clinicians understand which semen, hormonal, imaging, or clinical variables influence an AI output. However, these methods must be combined with privacy protection, bias assessment, laboratory-specific validation, and transparent reporting. AI should support, not replace, standardized semen analysis, urological evaluation, and patient-centered counseling. A responsible implementation pathway should align with medical-device regulation, reporting standards for AI studies, and local audits of performance across diverse populations.