{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T10:08:18Z","timestamp":1788775698126,"version":"build-2803163510"},"reference-count":111,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>Artificial intelligence (AI) has advanced rapidly across medicine, and IVF\u2014with its image-rich laboratory workflows, structured cycle data, and measurable endpoints\u2014is among the most amenable fields for integration. Three applications\u2014outcome prediction (T1), stimulation dosing (T2), and radio-frequency identification (RFID) quality tracking (T3)\u2014require 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 \u226540 years show the largest AI discriminatory advantage. Nine AI technologies (T1\u2013T9) were evaluated across the IVF clinical workflow using four implementation frameworks (TRL\/MLTRL, IDEAL, Rogers\u2019 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 (\u22645 years) comprises outcome prediction (T1), stimulation dosing (T2), and RFID quality tracking (T3), immediately deployable as decision-support adjuncts (12\u201318 months) pending prospective RCT validation. Wave 2 (5\u201310 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)\u2014miscarriage prediction (T8) and robotic ICSI (T9)\u2014comprises horizon technologies contingent on ethical and engineering resolution. Embryo-selection AUC rises with maternal age from 0.596 (&amp;lt;35 years) to 0.768 (\u226543 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\u2014not validation\u2014is 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.<\/jats:p>","DOI":"10.3389\/fendo.2026.1910448","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T09:16:57Z","timestamp":1788772617000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Implementation readiness of artificial intelligence in in vitro fertilization: a multi-framework appraisal with special reference to advanced reproductive age patients"],"prefix":"10.3389","volume":"17","author":[{"given":"Chia Lin","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Linkou Medical Center","place":["Taoyuan, Taiwan"]},{"name":"Chang Gung University College of Medicine","place":["Taoyuan, 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