{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T02:14:53Z","timestamp":1792808093075,"version":"4.5.0"},"reference-count":119,"publisher":"Georg Thieme Verlag KG","issue":"02","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":129,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Semin Reprod Med"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Although in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report delivery require specialized human and platform expertise. This review article takes a pragmatic approach to review relevant reports of IVF prognostic models and draws from extensive experience meeting patients' and providers' needs with the development of data and model pipelines to implement validated ML models at scale, at the point-of-care. Requirements of using ML-based IVF prognostics at point-of-care will be considered alongside clinical ML implementation factors critical for success. Finally, we discuss health, social, and economic objectives that may be achieved by leveraging combined human expertise and ML prognostics to expand fertility care access and advance health and social good.<\/jats:p>","DOI":"10.1055\/s-0044-1791536","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T23:46:00Z","timestamp":1728431160000},"page":"112-129","source":"Crossref","is-referenced-by-count":10,"title":["Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist"],"prefix":"10.1055","volume":"42","author":[{"given":"Mylene W.M.","family":"Yao","sequence":"additional","affiliation":[{"name":"R&D Department, Univfy, Los Altos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julian","family":"Jenkins","sequence":"additional","affiliation":[{"name":"Jencap Consulting Ltd., Cardiff, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth T.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"R&D Department, Univfy, Los Altos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Trevor","family":"Swanson","sequence":"additional","affiliation":[{"name":"R&D Department, Univfy, Los Altos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Menabrito","sequence":"additional","affiliation":[{"name":"R&D Department, Univfy, Los Altos, California"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"issue":"13","key":"ref1","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1056\/NEJMra2302038","article-title":"Artificial intelligence and machine learning in clinical medicine, 2023","volume":"388","author":"C J Haug","year":"2023","journal-title":"N Engl J Med"},{"issue":"04","key":"ref2","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.rbmo.2020.07.005","article-title":"Empathetic application of machine learning may address appropriate utilization of ART","volume":"41","author":"J Jenkins","year":"2020","journal-title":"Reprod Biomed Online"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"1377011","DOI":"10.3389\/frai.2024.1377011","article-title":"Ethics and responsible AI deployment","volume":"7","author":"P Radanliev","year":"2024","journal-title":"Front Artif Intell"},{"issue":"1150","key":"ref5","doi-asserted-by":"crossref","first-page":"20220934","DOI":"10.1259\/bjr.20220934","article-title":"AI and machine learning ethics, law, diversity, and global impact","volume":"96","author":"K Drabiak","year":"2023","journal-title":"Br J Radiol"},{"issue":"01","key":"ref14","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.fertnstert.2021.02.019","article-title":"Disparities in access to effective treatment for infertility in the United States: an Ethics Committee opinion","volume":"116","author":"Ethics Committee of the American Society for Reproductive Medicine. 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