Abstract
Artificial intelligence (AI) has advanced rapidly across medicine, and IVF—with its image-rich laboratory workflows, structured cycle data, and measurable endpoints—is among the most amenable fields for integration. Three applications—outcome prediction (T1), stimulation dosing (T2), and radio-frequency identification (RFID) quality tracking (T3)—require no new capital hardware and are immediately deployment-ready, and as professional adoption rose from 24.8% (2022) to 53.2% (2025), the field reached a stratified state of readiness. AI in IVF presents three implementation-relevant findings: the highest-profile technology (embryo selection) is not the most deployment-ready; the lowest-profile (outcome prediction) has the greatest near-term impact; and patients ≥40 years show the largest AI discriminatory advantage. Nine AI technologies (T1–T9) were evaluated across the IVF clinical workflow using four implementation frameworks (TRL/MLTRL, IDEAL, Rogers’ Diffusion of Innovation, NASSS) with OCEBM evidence grading, organized by deployment readiness to derive a deployable toolkit for the 2026 clinic and a synthesis for advanced reproductive age (ARA) patients. Three deployment waves emerged. Wave 1 (≤5 years) comprises outcome prediction (T1), stimulation dosing (T2), and RFID quality tracking (T3), immediately deployable as decision-support adjuncts (12–18 months) pending prospective RCT validation. Wave 2 (5–10 years) comprises AI ultrasound monitoring (T4), sperm analysis and selection (T5), embryo assessment (T6), and endometrial receptivity (T7), positioned for pilot-stage deployment with FDA-cleared FOLLISCAN (T4) leading. Wave 3 (beyond 2031)—miscarriage prediction (T8) and robotic ICSI (T9)—comprises horizon technologies contingent on ethical and engineering resolution. Embryo-selection AUC rises with maternal age from 0.596 (<35 years) to 0.768 (≥43 years; 29% relative increase), positioning ARA patients as candidates for greater AI value pending prospective multicenter validation, while platform-specific findings from the two largest embryo-selection RCTs (LOTUS met endpoint, n=442; iDAScore failed non-inferiority, n=1,066) indicate embryo assessment must be evaluated by platform, not as a uniform category. The three-wave framework lets clinic directors sequence AI investment by readiness; for Wave 1, procurement—not validation—is rate-limiting, and four prerequisite clusters (multicenter RCT completion, regulatory harmonization, ethical frameworks for AI-informed transfer, and workforce/IT infrastructure) must advance in parallel. ARA patients constitute the primary stress test, and ultimately success depends not on algorithmic sophistication but on rigorous validation, governance, and equitable deployment.