{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T04:04:07Z","timestamp":1792123447367,"version":"4.4.0"},"reference-count":101,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T00:00:00Z","timestamp":1669593600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and\/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile\/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.<\/jats:p>","DOI":"10.3390\/diagnostics12122979","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T08:43:03Z","timestamp":1669624983000},"page":"2979","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes\u2014The Value of Regulatory Frameworks"],"prefix":"10.3390","volume":"12","author":[{"given":"Sanja","family":"Medenica","sequence":"first","affiliation":[{"name":"Department of Endocrinology, Internal Medicine Clinic, Clinical Center of Montenegro, School of Medicine, University of Montenegro, 81000 Podgorica, Montenegro"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2689-1089","authenticated-orcid":false,"given":"Dusan","family":"Zivanovic","sequence":"additional","affiliation":[{"name":"Clinic of Endocrinology, Diabetes and Metabolic Disorders, University Clinical Center of Serbia, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5835-8684","authenticated-orcid":false,"given":"Ljubica","family":"Batkoska","sequence":"additional","affiliation":[{"name":"Medical Faculty, Ss. Cyril and Methodius University of Skopje, 1000 Skopje, North Macedonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susanna","family":"Marinelli","sequence":"additional","affiliation":[{"name":"School of Law, Polytecnic University of Marche, 60121 Ancona, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Basile","sequence":"additional","affiliation":[{"name":"IRCCS Orthopedic Institute Galeazzi, 20161 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Perino","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaspare","family":"Cucinella","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4269-4269","authenticated-orcid":false,"given":"Giuseppe","family":"Gullo","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5741-7139","authenticated-orcid":false,"given":"Simona","family":"Zaami","sequence":"additional","affiliation":[{"name":"Department of Anatomical, Histological, Forensic and Orthopedic Sciences, \u201cSapienza\u201d University of Rome, 00161 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,28]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (WHO) (2018). International Classification of Diseases, WHO. 11th Revision (ICD-11)."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1001\/jama.2021.4788","article-title":"Diagnosis and Management of Infertility","volume":"326","author":"Carson","year":"2021","journal-title":"JAMA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/S0140-6736(20)32667-2","article-title":"Male infertility","volume":"397","author":"Agarwal","year":"2020","journal-title":"Lancet"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"(2019). Infertility Workup for the Women\u2019s Health Specialist. Obstet. Gynecol., 133, 1294\u20131295.","DOI":"10.1097\/AOG.0000000000003272"},{"key":"ref_5","first-page":"CD010287","article-title":"Aromatase inhibitors (letrozole) for ovulation induction in infertile women with polycystic ovary syndrome","volume":"9","author":"Franik","year":"2022","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_6","first-page":"257","article-title":"Medical and Surgical Treatment of Reproductive Outcomes in Polycystic Ovary Syndrome: An Overview of Systematic Reviews","volume":"13","author":"Gadalla","year":"2019","journal-title":"Int. J. Fertil. Steril."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Medenica, S., Garalejic, E., Arsic, B., Medjo, B., Jovic, D.B., Abazovic, D., Vukovic, R., and Zarkovic, M. (2018). Follicular fluid thyroid autoantibodies, thyrotropin, free thyroxine levels and assisted reproductive technology outcome. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0206652"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.rbmo.2011.10.011","article-title":"Cryptic sperm defects may be the cause for total fertilization failure in oocyte donor cycles","volume":"24","author":"Goudakou","year":"2012","journal-title":"Reprod. Biomed. Online"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1159\/000512790","article-title":"2021 European Thyroid Association Guideline on Thyroid Disorders prior to and during Assisted Reproduction","volume":"9","author":"Poppe","year":"2020","journal-title":"Eur. Thyroid. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4842316","DOI":"10.1155\/2022\/4842316","article-title":"The Role of Cell and Gene Therapies in the Treatment of Infertility in Patients with Thyroid Autoimmunity","volume":"2022","author":"Medenica","year":"2022","journal-title":"Int. J. Endocrinol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.ejogrb.2012.03.003","article-title":"Injection of embryo culture supernatant to the endometrial cavity does not affect outcomes in IVF\/ICSI or oocyte donation cycles: A randomized clinical trial","volume":"162","author":"Prapas","year":"2012","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1038\/s41572-018-0058-8","article-title":"Female subfertility","volume":"5","author":"Farquhar","year":"2019","journal-title":"Nat. Rev. Dis. Prim."},{"key":"ref_13","first-page":"100224","article-title":"Study on artificial intelligence: The state of the art and future prospects","volume":"23","author":"Zhang","year":"2021","journal-title":"J. Ind. Inf. Integr."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1109\/JAS.2020.1003021","article-title":"Artificial intelligence applications in the development of autonomous vehicles: A survey","volume":"7","author":"Ma","year":"2020","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abduljabbar, R., Dia, H., Liyanage, S., and Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11.","DOI":"10.3390\/su11010189"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s00521-010-0362-z","article-title":"A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems","volume":"19","author":"Bahrammirzaee","year":"2010","journal-title":"Neural Comput. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1109\/TASLP.2014.2339736","article-title":"Convolutional Neural Networks for Speech Recognition","volume":"22","author":"Mohamed","year":"2014","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 23\u201328). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"l1171","DOI":"10.1136\/bmj.l1171","article-title":"Lethal autonomous weapons","volume":"364","author":"Javorsky","year":"2019","journal-title":"BMJ"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/978-94-6265-343-6_3","article-title":"Lethal Autonomous Weapon Systems and Their Compatibility with International Humanitarian Law: A Primer on the Debate","volume":"Volume 21","author":"Gill","year":"2020","journal-title":"Yearbook of International Humanitarian Law, Volume 21 (2018)"},{"key":"ref_21","unstructured":"Ravenscroft, I. (2005). Philosophy of Mind: A Beginner's Guide, Oxford University Press."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Sagiroglu, D.S., and Sinanc, D. (2013, January 20\u201324). Big data: A review. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems [CTS], San Diego, CA, USA.","DOI":"10.1109\/CTS.2013.6567202"},{"key":"ref_23","unstructured":"Marr, B. (2022, September 30). How Much Data Do We Create Every Day?. The Mind-Blowing Stats Everyone Should Read, Available online: https:\/\/bernardmarr.com\/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read\/."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1136\/amiajnl-2011-000464","article-title":"Natural language processing: An introduction","volume":"18","author":"Nadkarni","year":"2011","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_25","first-page":"381","article-title":"Machine Learning Algorithms\u2014A Review","volume":"9","author":"Mahesh","year":"2018","journal-title":"Int. J. Sci. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","article-title":"Artificial intelligence in medicine","volume":"69","author":"Hamet","year":"2017","journal-title":"Metabolism"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.acra.2019.10.001","article-title":"Artificial Intelligence in Medicine: Where Are We Now?","volume":"27","author":"Kulkarni","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"R139","DOI":"10.1530\/REP-18-0523","article-title":"Artificial intelligence in reproductive medicine","volume":"158","author":"Wang","year":"2019","journal-title":"Reproduction"},{"key":"ref_29","unstructured":"Singh, A., Thakur, N., and Sharma, A. (2016, January 16\u201318). A review of supervised machine learning algorithms. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development [INDIACom], New Delhi, India."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1093\/humrep\/deaa013","article-title":"Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF","volume":"35","author":"VerMilyea","year":"2020","journal-title":"Hum. Reprod."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1675","DOI":"10.1007\/s10815-021-02254-6","article-title":"Embryo selection with artificial intelligence: How to evaluate and compare methods?","volume":"38","author":"Kragh","year":"2021","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"924539","DOI":"10.3389\/fpubh.2022.924539","article-title":"Multi-Omics Analysis and Machine Learning Prediction Model for Pregnancy Outcomes After Intracytoplasmic Sperm Injection\u2013in vitro Fertilization","volume":"10","author":"Chen","year":"2022","journal-title":"Front. Public Health"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gentleman, R., and Carey, V.J. (2008). Unsupervised Machine Learning. Bioconductor Case Studies, Springer.","DOI":"10.1007\/978-0-387-77240-0_10"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"7","DOI":"10.2478\/slgr-2014-0043","article-title":"The Use of Principal Component Analysis and Logistic Regression in Prediction of Infertility Treatment Outcome","volume":"39","author":"Milewska","year":"2014","journal-title":"Stud. Logic. Gramm. Rhetor."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1111\/jcmm.16288","article-title":"Application of convolutional neural network on early human embryo segmentation during in vitro fertilization","volume":"25","author":"Zhao","year":"2021","journal-title":"J. Cell. Mol. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.rbmo.2021.02.015","article-title":"Deep learning neural network analysis of human blastocyst expansion from time-lapse image files","volume":"42","author":"Huang","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11701-017-0682-4","article-title":"Robotic-assisted laparoscopy in reproductive surgery: A contemporary review","volume":"11","author":"Jayakumaran","year":"2017","journal-title":"J. Robot. Surg."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1016\/j.suc.2019.12.007","article-title":"Robotic Surgery in Gynecology","volume":"100","author":"Moon","year":"2020","journal-title":"Surg. Clin. North Am."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1038\/aja.2012.131","article-title":"Robotic assisted andrological surgery","volume":"15","author":"Parekattil","year":"2012","journal-title":"Asian J. Androl."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, C.-W., Kuo, C.-Y., Chen, C.-H., Hsieh, Y.-H., and Su, E.C.-Y. (2022). Predicting clinical pregnancy using clinical features and machine learning algorithms in in vitro fertilization. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0267554"},{"key":"ref_42","unstructured":"Practice Committee of the American Society for Reproductive Medicine (2021). Practice Committee of the American Society for Reproductive Medicine. Fertility evaluation of infertile women: A committee opinion. Fertil. Steril., 116, 1255\u20131265."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"745039","DOI":"10.3389\/fendo.2021.745039","article-title":"Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm","volume":"12","author":"Liu","year":"2021","journal-title":"Front. Endocrinol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"20925","DOI":"10.1038\/s41598-020-76928-z","article-title":"Machine learning predicts live-birth occurrence before in-vitro fertilization treatment","volume":"10","author":"Goyal","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105624","DOI":"10.1016\/j.cmpb.2020.105624","article-title":"Machine learning algorithms to predict early pregnancy loss after in vitro fertilization-embryo transfer with fetal heart rate as a strong predictor","volume":"196","author":"Liu","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1007\/s10815-019-01498-7","article-title":"An artificial neural network for the prediction of assisted reproduction outcome","volume":"36","author":"Vogiatzi","year":"2019","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.1002\/jum.15827","article-title":"Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review","volume":"41","author":"Chen","year":"2021","journal-title":"J. Ultrasound Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/s40738-020-00094-z","article-title":"Evaluation of oocyte quality in Polycystic ovary syndrome patients undergoing ART cycles","volume":"7","author":"Nikbakht","year":"2021","journal-title":"Fertil. Res. Pract."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"907874","DOI":"10.3389\/fendo.2022.907874","article-title":"An endometrial receptivity scoring system basing on the endometrial thickness, volume, echo, peristalsis, and blood flow evaluated by ultrasonography","volume":"13","author":"Zhang","year":"2022","journal-title":"Front. Endocrinol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kiruthika, V., and Ramya, M. (2014, January 8\u201310). Automatic Segmentation of Ovarian Follicle Using K-Means Clustering. Proceedings of the 2014 Fifth International Conference on Signal and Image Processing, Bangalore, India.","DOI":"10.1109\/ICSIP.2014.27"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Faghih, R.T., Styer, A.K., and Brown, E.N. (2017, January 11\u201315). Automated ovarian follicular monitoring: A novel real-time approach. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8036904"},{"key":"ref_52","unstructured":"Chen, T., Zhang, W., Good, S., Zhou, K.S., and Comaniciu, D. (October, January 29). Automatic ovarian follicle quantification from 3D ultrasound data using global\/local context with database guided segmentation. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12967-020-02660-x","article-title":"Deep learning model for classifying endometrial lesions","volume":"19","author":"Zhang","year":"2021","journal-title":"J. Transl. Med."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102883","DOI":"10.1016\/j.pdpdt.2022.102883","article-title":"Determination of idiopathic female infertility from infrared spectra of follicle fluid combined with gonadotrophin levels, multivariate analysis and machine learning methods","volume":"38","author":"Jakubczyk","year":"2022","journal-title":"Photodiagn. Photodyn. Ther."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1007\/s43032-021-00723-y","article-title":"Human Oocyte Morphology and Outcomes of Infertility Treatment: A Systematic Review","volume":"29","author":"Nikiforov","year":"2021","journal-title":"Reprod. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.fertnstert.2019.05.019","article-title":"Artificial intelligence: Its applications in reproductive medicine and the assisted reproductive technologies","volume":"112","author":"Zaninovic","year":"2019","journal-title":"Fertil. Steril."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lee, D., and Yoon, S.N. (2021). Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18010271"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1232","DOI":"10.1016\/j.fertnstert.2020.08.023","article-title":"Novel and conventional embryo parameters as input data for artificial neural networks: An artificial intelligence model applied for prediction of the implantation potential","volume":"114","author":"Bori","year":"2020","journal-title":"Fertil. Steril."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1038\/s41746-019-0089-x","article-title":"Towards a gamete matching platform: Using immunogenetics and artificial intelligence to predict recurrent miscarriage","volume":"2","author":"Nowak","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.rbmo.2014.10.013","article-title":"Reporting of embryo transfer methods in IVF research: A cross-sectional study","volume":"30","author":"Gambadauro","year":"2015","journal-title":"Reprod. Biomed. Online"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1001\/jama.282.19.1832","article-title":"Live-birth rates and multiple-birth risk using in vitro fertilization","volume":"282","author":"Schieve","year":"1999","journal-title":"JAMA"},{"key":"ref_62","unstructured":"European IVF-Monitoring Consortium (EIM), European Society of Human Reproduction and Embryology (ESHRE), Kupka, M.S., D\u2019Hooghe, T., Ferraretti, A.P., de Mouzon, J., Erb, K., Castilla, J.A., Calhaz-Jorge, C., and De Geyter, C. (2016). Assisted reproductive technology in Europe, 2011: Results generated from European registers by ESHRE. Hum. Reprod., 31, 233\u2013248."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/ijgo.13168","article-title":"Management of multiple gestations","volume":"150","author":"Breslin","year":"2020","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2581","DOI":"10.1093\/humrep\/14.10.2581","article-title":"Prevention of twin pregnancy after in-vitro fertilization or intracytoplasmic sperm injection based on strict embryo criteria: A prospective randomized clinical trial","volume":"14","author":"Gerris","year":"1999","journal-title":"Hum. Reprod."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s12958-016-0145-1","article-title":"Selecting the embryo with the highest implantation potential using a data mining based prediction model","volume":"14","author":"Chen","year":"2016","journal-title":"Reprod. Biol. Endocrinol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1093\/humrep\/des079","article-title":"Time-lapse monitoring as a tool for clinical embryo assessment","volume":"27","author":"Kirkegaard","year":"2012","journal-title":"Hum. Reprod."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.fertnstert.2016.12.005","article-title":"Which factors are most predictive for live birth after in vitro fertilization and intracytoplasmic sperm injection (IVF\/ICSI) treatments? Analysis of 100 prospectively recorded variables in 8,400 IVF\/ICSI single-embryo transfers","volume":"107","author":"Vaegter","year":"2017","journal-title":"Fertil. Steril."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1093\/humrep\/dew188","article-title":"Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3","volume":"31","author":"Petersen","year":"2016","journal-title":"Hum. Reprod."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1016\/S1472-6483(10)62069-X","article-title":"Infertility therapy-associated multiple pregnancies (births): An ongoing epidemic","volume":"7","author":"Adashi","year":"2003","journal-title":"Reprod. Biomed. Online"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1810","DOI":"10.1177\/1460458219892138","article-title":"Computational prediction of implantation outcome after embryo transfer","volume":"26","author":"Raef","year":"2019","journal-title":"Health Inform. J."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.ejogrb.2017.04.031","article-title":"Cost-effectiveness of single versus double embryo transfer in IVF in relation to female age","volume":"214","author":"Moolenaar","year":"2017","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"885661","DOI":"10.3389\/fphys.2022.885661","article-title":"The Application of Artificial Intelligence in Predicting Embryo Transfer Outcome of Recurrent Implantation Failure","volume":"13","author":"Shen","year":"2022","journal-title":"Front. Physiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"670","DOI":"10.12688\/f1000research.17076.1","article-title":"Recent advances in understanding and managing male infertility","volume":"8","author":"Fainberg","year":"2019","journal-title":"F1000Research"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1768","DOI":"10.1093\/humrep\/16.8.1768","article-title":"Contribution of environmental factors to the risk of male infertility","volume":"16","author":"Oliva","year":"2001","journal-title":"Hum. Reprod."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"531","DOI":"10.3233\/THC-140816","article-title":"Seminal quality prediction using data mining methods","volume":"22","author":"Sahoo","year":"2014","journal-title":"Technol. Health Care"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"12564","DOI":"10.1016\/j.eswa.2012.05.028","article-title":"Predicting seminal quality with artificial intelligence methods","volume":"39","author":"Gil","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"167","DOI":"10.32725\/jab.2019.015","article-title":"Prediction of semen quality using artificial neural network","volume":"17","author":"Badura","year":"2019","journal-title":"J. Appl. Biomed."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1080\/19396368.2016.1185654","article-title":"Validation of artificial neural network models for predicting biochemical markers associated with male infertility","volume":"62","author":"Vickram","year":"2016","journal-title":"Syst. Biol. Reprod. Med."},{"key":"ref_79","unstructured":"World Health Organization (2021). WHO Laboratory Manual for the Examination and Processing of Human Semen, World Health Organization. [6th ed.]. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240030787."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1007\/s11934-019-0914-4","article-title":"Artificial Intelligence in Reproductive Urology","volume":"20","author":"Chu","year":"2019","journal-title":"Curr. Urol. Rep."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10815-013-0139-2","article-title":"Double-blind prospective study comparing two automated sperm analyzers versus manual semen assessment","volume":"31","author":"Lammers","year":"2013","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"16770","DOI":"10.1038\/s41598-019-53217-y","article-title":"Machine Learning-Based Analysis of Sperm Videos and Participant Data for Male Fertility Prediction","volume":"9","author":"Hicks","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"104061","DOI":"10.1016\/j.compbiomed.2020.104061","article-title":"Quantification of human sperm concentration using machine learning-based spectrophotometry","volume":"127","author":"Lesani","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_84","unstructured":"Haugen, T.B., Andersen, J.M., Witczak, O., Hammer, H.L., Hicks, S.A., Borgli, R.J., Halvorsen, P., and Riegler, M.A. (2022, September 30). VISEM: A Multimodal Video Dataset of Human Spermatozoa. Available online: http:\/\/rgdoi.net\/10.13140\/RG.2.2.16104.93444."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.1016\/j.fertnstert.2018.08.049","article-title":"Home sperm testing device versus laboratory sperm quality analyzer: Comparison of motile sperm concentration","volume":"110","author":"Agarwal","year":"2018","journal-title":"Fertil. Steril."},{"key":"ref_86","first-page":"1","article-title":"Unexplained Infertility, the Controversial Matter in Management of Infertile Couples","volume":"16","author":"Sadeghi","year":"2015","journal-title":"J. Reprod. Infertil."},{"key":"ref_87","first-page":"19","article-title":"Lifestyle-Related Factors Associated with Reproductive Health in Couples Seeking Fertility Treatments: Results of A Pilot Study","volume":"12","author":"Babineau","year":"2018","journal-title":"Int. J. Fertil. Steril."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1016\/j.fertnstert.2017.01.017","article-title":"Obesity and female infertility: Potential mediators of obesity's impact","volume":"107","author":"Broughton","year":"2017","journal-title":"Fertil. Steril."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41367-019-0009-1","article-title":"Female infertility: Which role for obesity?","volume":"9","author":"Gambineri","year":"2019","journal-title":"Int. J. Obes. Suppl."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"24003","DOI":"10.1038\/s41598-021-03165-3","article-title":"Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility","volume":"11","author":"Bachelot","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1163\/15718093-12530378","article-title":"Gamete Donation: Are Children Entitled to Know Their Genetic Origins? A Comparison of Opposing Views. The Italian State of Affairs","volume":"25","author":"Marinelli","year":"2018","journal-title":"Eur. J. Health Law"},{"key":"ref_92","first-page":"360","article-title":"Advantages and limitations of artificial intelligence","volume":"6","author":"Chowdhury","year":"2012","journal-title":"Artif. Intell. Appl. Crit. Transp. Issues"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.jocn.2019.03.001","article-title":"Medical ethics considerations on artificial intelligence","volume":"64","author":"Keskinbora","year":"2019","journal-title":"J. Clin. Neurosci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"108768","DOI":"10.1016\/j.ejrad.2019.108768","article-title":"Ethical considerations in artificial intelligence","volume":"122","author":"Safdar","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"hoab040","DOI":"10.1093\/hropen\/hoab040","article-title":"Interpretable, not black-box, artificial intelligence should be used for embryo selection","volume":"4","author":"Afnan","year":"2021","journal-title":"Hum. Reprod. Open"},{"key":"ref_96","unstructured":"European Commission Press Release (2022, October 03). Member States and Commission to Work Together to Boost Artificial Intelligence \u201cMade in Europe\u201d. Issued on 7 December 2018. Available online: https:\/\/ec.europa.eu\/commission\/presscorner\/detail\/en\/IP_18_6689."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"257","DOI":"10.2471\/BLT.19.237289","article-title":"How to achieve trustworthy artificial intelligence for health","volume":"98","author":"Henden","year":"2020","journal-title":"Bull. World Health Organ."},{"key":"ref_98","unstructured":"Independent High-Level Expert Group on Artificial Intelligence Set Up by the European Commission (2022, October 03). Ethics Guidelines for Trustworthy AI. Issued on 8 April 2019. Available online: https:\/\/ec.europa.eu\/futurium\/en\/ai-alliance-consultation.1.html."},{"key":"ref_99","unstructured":"(2022, October 03). WHO Guidance. Ethics and Governance of Artificial Intelligence for Health. Issued on 28 June 2021. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789240029200."},{"key":"ref_100","first-page":"326","article-title":"On the Ethics and Practicalities of Artificial Intelligence, Risk Assessment, and Race","volume":"49","author":"Hogan","year":"2021","journal-title":"J. Am. Acad. Psychiatry Law"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Amann, J., Blasimme, A., Vayena, E., Frey, D., and Madai, V.I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. 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