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

Area of Science:

• Andrology
• Medical Informatics
• Reproductive Medicine

Background:

• Artificial intelligence (AI) is increasingly utilized in male infertility diagnostics and treatment.
• Applications include semen analysis, sperm selection, and predicting sperm retrieval success.
• Clinical adoption requires more than algorithmic innovation, focusing on explainability, governance, and integration.

Purpose of the Study:

• To propose a practical roadmap for implementing explainable AI in male infertility care.
• To guide the transition from laboratory support to validated predictive tools and shared decision-making.
• To outline essential considerations for responsible AI integration in andrology.

Main Methods:

• Discusses explainability techniques like feature importance, SHAP, LIME, and saliency maps.
• Highlights the necessity of combining explainability with privacy, bias assessment, and validation.
• Emphasizes a phased approach from lab quality support to clinical prediction tools.

Main Results:

• Explainable AI can help clinicians interpret AI-driven outputs by identifying influential variables (semen, hormonal, imaging, clinical).
• Responsible AI implementation requires robust validation, bias assessment, and transparent reporting.
• AI should augment, not replace, standard clinical practices like semen analysis and patient counseling.

Conclusions:

• A pragmatic roadmap for explainable AI in male infertility is proposed, prioritizing clinical utility and safety.
• Responsible AI integration necessitates adherence to medical device regulations, reporting standards, and local performance audits.
• The focus should be on AI as a supportive tool within a comprehensive andrology framework.