{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:20:03Z","timestamp":1792790403766,"version":"4.5.0"},"reference-count":89,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Female infertility is a multifaceted condition affecting millions of women worldwide, with causes ranging from hormonal imbalances and genetic predispositions to lifestyle and environmental factors. Traditional diagnostic approaches, such as hormonal assays, ultrasound imaging, and genetic testing, often require extensive time, resources, and expert interpretation. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of reproductive medicine, offering advanced capabilities for improving the accuracy, efficiency, and personalization of infertility diagnosis and treatment. AI technologies demonstrate significant potential in analyzing vast and complex datasets, identifying hidden patterns, and providing data-driven insights that enhance clinical decision-making processes in assisted reproductive technologies (ART) services. This narrative review explores the current advancements in AI applications in female infertility diagnostics and therapeutics, highlighting key technological innovations, their clinical implications, and existing limitations. It also discusses the future potential of AI in revolutionizing reproductive healthcare. As AI-based technologies continue to evolve, their integration into reproductive medicine is expected to pave the way for more accessible, cost-effective, and personalized fertility care.<\/jats:p>","DOI":"10.3390\/jcm14093127","type":"journal-article","created":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T09:16:12Z","timestamp":1746090972000},"page":"3127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["The Role of Artificial Intelligence in Female Infertility Diagnosis: An Update"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9362-5912","authenticated-orcid":false,"given":"Necati","family":"Findikli","sequence":"first","affiliation":[{"name":"Fertility Clinic, Department of Obstetrics and Gynecology, Universit\u00e9 Libre de Bruxelles (ULB), H\u00f4pital Universitaire de Bruxelles (H.U.B), CUB H\u00f4pital Erasme, Route de Lennik 808, 1070 Bruxelles, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Catherine","family":"Houba","sequence":"additional","affiliation":[{"name":"Fertility Clinic, Department of Obstetrics and Gynecology, Universit\u00e9 Libre de Bruxelles (ULB), H\u00f4pital Universitaire de Bruxelles (H.U.B), CUB H\u00f4pital Erasme, Route de Lennik 808, 1070 Bruxelles, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7221-4361","authenticated-orcid":false,"given":"David","family":"Pening","sequence":"additional","affiliation":[{"name":"Fertility Clinic, Department of Obstetrics and Gynecology, Universit\u00e9 Libre de Bruxelles (ULB), H\u00f4pital Universitaire de Bruxelles (H.U.B), CUB H\u00f4pital Erasme, Route de Lennik 808, 1070 Bruxelles, Belgium"},{"name":"Research Laboratory on Human Reproduction, Universit\u00e9 Libre de Bruxelles (ULB), H\u00f4pital Universitaire de Bruxelles (H.U.B), CUB H\u00f4pital Erasme, Route de Lennik 808, 1070 Bruxelles, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7236-0687","authenticated-orcid":false,"given":"Anne","family":"Delbaere","sequence":"additional","affiliation":[{"name":"Fertility Clinic, Department of Obstetrics and Gynecology, Universit\u00e9 Libre de Bruxelles (ULB), H\u00f4pital Universitaire de Bruxelles (H.U.B), CUB H\u00f4pital Erasme, Route de Lennik 808, 1070 Bruxelles, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Medenica, S., Zivanovic, D., Batkoska, L., Marinelli, S., Basile, G., Perino, A., Cucinella, G., Gullo, G., and Zaami, S. (2022). The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks. Diagn. Basel Switz., 12.","DOI":"10.3390\/diagnostics12122979"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.clinbiochem.2018.03.012","article-title":"Fertility and Infertility: Definition and Epidemiology","volume":"62","author":"Wyns","year":"2018","journal-title":"Clin. Biochem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1001\/jama.2021.4788","article-title":"Diagnosis and Management of Infertility: A Review","volume":"326","author":"Carson","year":"2021","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10815-016-0836-8","article-title":"Female Infertility, Infertility-Associated Diagnoses, and Comorbidities: A Review","volume":"34","author":"Hanson","year":"2017","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1080\/09513590.2019.1650345","article-title":"Decision Points for Individualized Hormonal Stimulation with Recombinant Gonadotropins for Treatment of Women with Infertility","volume":"35","author":"Lunenfeld","year":"2019","journal-title":"Gynecol. Endocrinol. Off. J. Int. Soc. Gynecol. Endocrinol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1193","DOI":"10.1109\/JBHI.2015.2450362","article-title":"Big Data for Health","volume":"19","author":"Poon","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, Z., Li, M., and Peng, W. (2023). Exploring Biomarkers of Premature Ovarian Insufficiency Based on Oxford Nanopore Transcriptional Profile and Machine Learning. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-38754-x"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Qu, Y., Chen, M., Wang, Y., Qu, L., Wang, R., Liu, H., Wang, L., and Nie, Z. (2024). Rapid Screening of Infertility-Associated Gynecological Conditions via Ambient Glow Discharge Mass Spectrometry Utilizing Urine Metabolic Fingerprints. Talanta, 274.","DOI":"10.1016\/j.talanta.2024.125969"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jakubczyk, P., Paja, W., Pancerz, K., Cebulski, J., Depciuch, J., Uzun, \u00d6., Tarhan, N., and Guleken, Z. (2022). Determination of Idiopathic Female Infertility from Infrared Spectra of Follicle Fluid Combined with Gonadotrophin Levels, Multivariate Analysis and Machine Learning Methods. Photodiagnosis Photodyn. Ther., 38.","DOI":"10.1016\/j.pdpdt.2022.102883"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"687","DOI":"10.33549\/physiolres.935389","article-title":"Reproductomics: Exploring the Applications and Advancements of Computational Tools","volume":"73","author":"Sengupta","year":"2024","journal-title":"Physiol. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.ejogrb.2023.10.015","article-title":"New Class of RNA Biomarker for Endometriosis Diagnosis: The Potential of Salivary piRNA Expression","volume":"291","author":"Dabi","year":"2023","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1007\/s10815-022-02578-x","article-title":"Aging Endometrium in Young Women: Molecular Classification of Endometrial Aging-Based Markers in Women Younger than 35 Years with Recurrent Implantation Failure","volume":"39","author":"Chen","year":"2022","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_13","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":"Reprod. Camb. Engl."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hanassab, S., Abbara, A., Yeung, A.C., Voliotis, M., Tsaneva-Atanasova, K., Kelsey, T.W., Trew, G.H., Nelson, S.M., Heinis, T., and Dhillo, W.S. (2024). The Prospect of Artificial Intelligence to Personalize Assisted Reproductive Technology. NPJ Digit. Med., 7.","DOI":"10.1038\/s41746-024-01006-x"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cai, J., Chen, S., Guo, S., Wang, S., Li, L., Liu, X., Zheng, K., Liu, Y., and Chen, S. (2023). RegEMR: A Natural Language Processing System to Automatically Identify Premature Ovarian Decline from Chinese Electronic Medical Records. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02239-8"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1016\/j.rbmo.2022.03.036","article-title":"Computer Software (SiD) Assisted Real-Time Single Sperm Selection Associated with Fertilization and Blastocyst Formation","volume":"45","author":"Drakeley","year":"2022","journal-title":"Reprod. Biomed. Online"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Huang, W.-L., Liao, S.-L., Huang, H.-L., Su, Y.-X., Jerng, J.-S., Lu, C.-Y., Ho, W.-S., and Xu, J.-R. (2024). A Case Study of Lean Digital Transformation through Robotic Process Automation in Healthcare. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-65715-9"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Practice Committee of the American Society for Reproductive Medicine (2021). Fertility Evaluation of Infertile Women: A Committee Opinion. Fertil. Steril., 116, 1255\u20131265.","DOI":"10.1016\/j.fertnstert.2021.08.038"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1016\/j.molmed.2024.06.008","article-title":"Endometriosis: Recent Advances That Could Accelerate Diagnosis and Improve Care","volume":"30","author":"Griffiths","year":"2024","journal-title":"Trends Mol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1055\/s-0044-1791536","article-title":"Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist","volume":"42","author":"Yao","year":"2024","journal-title":"Semin. Reprod. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2901","DOI":"10.1007\/s43032-024-01615-7","article-title":"Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review","volume":"31","author":"Verma","year":"2024","journal-title":"Reprod. Sci. Thousand Oaks Calif"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"17137","DOI":"10.18632\/aging.203032","article-title":"Machine-Intelligence for Developing a Potent Signature to Predict Ovarian Response to Tailor Assisted Reproduction Technology","volume":"13","author":"Yan","year":"2021","journal-title":"Aging"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Shanmugavadivel, K., Dhar, M.S., Manesh, T.R., Al-Shehari, T., Alsadhan, N.A., and Yimer, T.E. (2024). Optimized Polycystic Ovarian Disease Prognosis and Classification Using AI Based Computational Approaches on Multi-Modality Data. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02688-9"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zad, Z., Jiang, V.S., Wolf, A.T., Wang, T., Cheng, J.J., Paschalidis, I.C., and Mahalingaiah, S. (2024). Predicting Polycystic Ovary Syndrome with Machine Learning Algorithms from Electronic Health Records. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1298628"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, S., Arffman, R.K., Komsi, E.K., Lindgren, O., Kemppainen, J.A., Metsola, H., Rossi, H.-R., Ahtikoski, A., Kask, K., and Saare, M. (2024). AI-Algorithm Training and Validation for Identification of Endometrial CD138+ Cells in Infertility-Associated Conditions; Polycystic Ovary Syndrome (PCOS) and Recurrent Implantation Failure (RIF). J. Pathol. Inform., 15.","DOI":"10.1016\/j.jpi.2024.100380"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.ejogrb.2024.04.026","article-title":"Construction of Deep Learning-Based Convolutional Neural Network Model for Automatic Detection of Fluid Hysteroscopic Endometrial Micropolyps in Infertile Women with Chronic Endometritis","volume":"297","author":"Kitaya","year":"2024","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.fertnstert.2024.03.015","article-title":"Predicting Risk of Endometrial Failure: A Biomarker Signature That Identifies a Novel Disruption Independent of Endometrial Timing in Patients Undergoing Hormonal Replacement Cycles","volume":"122","author":"Spath","year":"2024","journal-title":"Fertil. Steril."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xiong, Z., Zhang, W., Liu, S., Liu, K., Wang, J., Qin, P., Liu, Y., and Jiang, Q. (2023). The Combination of CD138\/MUM1 Dual-Staining and Artificial Intelligence for Plasma Cell Counting in the Diagnosis of Chronic Endometritis. Am. J. Reprod. Immunol., 89.","DOI":"10.1111\/aji.13671"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rbmo.2022.09.019","article-title":"Endometriosis-Associated Infertility Diagnosis Based on Saliva microRNA Signatures","volume":"46","author":"Dabi","year":"2023","journal-title":"Reprod. Biomed. Online"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yu, L., and Qing, X. (2022). Diagnosis of Idiopathic Premature Ovarian Failure by Color Doppler Ultrasound under the Intelligent Segmentation Algorithm. Comput. Math. Methods Med., 2022.","DOI":"10.1155\/2022\/2645607"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Suha, S.A., and Islam, M.N. (2022). An Extended Machine Learning Technique for Polycystic Ovary Syndrome Detection Using Ovary Ultrasound Image. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-21724-0"},{"key":"ref_32","first-page":"174","article-title":"Artificial Intelligence Deep Learning Model Assessment of Leukocyte Counts and Proliferation in Endometrium from Women with and without Polycystic Ovary Syndrome","volume":"3","author":"Kangasniemi","year":"2022","journal-title":"FS Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Liu, N., and Ren, Q. (2021). Magnetic Resonance Imaging Feature Analysis and Evaluation of Tubal Patency under Convolutional Neural Network in the Diagnosis of Infertility. Contrast Media Mol. Imaging, 2021.","DOI":"10.1155\/2021\/5175072"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"516","DOI":"10.1016\/j.ajog.2024.05.028","article-title":"Consumer Wearables and Personal Devices for Tracking the Fertile Window","volume":"231","author":"Cromack","year":"2024","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yu, J.-L., Su, Y.-F., Zhang, C., Jin, L., Lin, X.-H., Chen, L.-T., Huang, H.-F., and Wu, Y.-T. (2022). Tracking of Menstrual Cycles and Prediction of the Fertile Window via Measurements of Basal Body Temperature and Heart Rate as Well as Machine-Learning Algorithms. Reprod. Biol. Endocrinol. RBE, 20.","DOI":"10.1186\/s12958-022-00993-4"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tan, S., Leonardi, M., Lo, G., and Lee, E. (2024). Role of Ultrasonography in the Diagnosis of Endometriosis in Infertile Women: Ovarian Endometrioma, Deep Endometriosis, and Superficial Endometriosis. Best Pract. Res. Clin. Obstet. Gynaecol., 92.","DOI":"10.1016\/j.bpobgyn.2023.102450"},{"key":"ref_37","first-page":"1343","article-title":"Artificial Intelligence in the Assessment of Female Reproductive Function Using Ultrasound: A Review","volume":"41","author":"Chen","year":"2022","journal-title":"J. Ultrasound Med. Off. J. Am. Inst. Ultrasound Med."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kitaya, K., Yasuo, T., and Yamaguchi, T. (2024). Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models. Med. Kaunas Lith., 60.","DOI":"10.3390\/medicina60060972"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., and Recker, F. (2023). Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology. J. Clin. Med., 12.","DOI":"10.3390\/jcm12216833"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1016\/j.rbmo.2022.07.012","article-title":"Evaluation of Oocyte Maturity Using Artificial Intelligence Quantification of Follicle Volume Biomarker by Three-Dimensional Ultrasound","volume":"45","author":"Liang","year":"2022","journal-title":"Reprod. Biomed. Online"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, N., Hao, T., Zhang, F., Ni, Q., Zhu, D., Wang, Y., Shi, Y., and Mi, X. (2024). Application of Machine Learning Techniques in the Diagnosis of Endometriosis. BMC Womens Health, 24.","DOI":"10.1186\/s12905-024-03334-2"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Xie, Z.-W., He, Y., Feng, Y.-X., and Wang, X.-H. (2024). Identification of Programmed Cell Death-Related Genes and Diagnostic Biomarkers in Endometriosis Using a Machine Learning and Mendelian Randomization Approach. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1372221"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ding, T., Ren, W., Wang, T., Han, Y., Ma, W., Wang, M., Fu, F., Li, Y., and Wang, S. (2023). Assessment and Quantification of Ovarian Reserve on the Basis of Machine Learning Models. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1087429"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1007\/s43032-024-01538-3","article-title":"The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators Can Predict Pregnancy Outcomes in Patients with PCOS","volume":"31","author":"Huang","year":"2024","journal-title":"Reprod. Sci. Thousand Oaks Calif"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1093\/humrep\/dead272","article-title":"ChatGPT: A Reliable Fertility Decision-Making Tool?","volume":"39","author":"Beilby","year":"2024","journal-title":"Hum. Reprod. Oxf. Engl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Devranoglu, B., Gurbuz, T., and Gokmen, O. (2024). ChatGPT\u2019s Efficacy in Queries Regarding Polycystic Ovary Syndrome and Treatment Strategies for Women Experiencing Infertility. Diagn. Basel Switz., 14.","DOI":"10.3390\/diagnostics14111082"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.fertnstert.2023.05.151","article-title":"The Promise and Peril of Using a Large Language Model to Obtain Clinical Information: ChatGPT Performs Strongly as a Fertility Counseling Tool with Limitations","volume":"120","author":"Chervenak","year":"2023","journal-title":"Fertil. Steril."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1097\/GME.0000000000002246","article-title":"Appropriateness of Premature Ovarian Insufficiency Recommendations Provided by ChatGPT","volume":"30","year":"2023","journal-title":"Menopause"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.ogc.2023.09.003","article-title":"The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies","volume":"50","author":"Jiang","year":"2023","journal-title":"Obstet. Gynecol. Clin. North Am."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, M., He, S., Wang, X., Liu, X., Xie, X., and Bao, H. (2024). Derivation and Validation of the First Web-Based Nomogram to Predict the Spontaneous Pregnancy after Reproductive Surgery Using Machine Learning Models. Front. Endocrinol., 15.","DOI":"10.3389\/fendo.2024.1378157"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, Y., Duan, Y., Yuan, X., Cai, B., Xu, Y., and Yuan, Y. (2021). A Novel Nomogram for Individualized Gonadotropin Starting Dose in GnRH Antagonist Protocol. Front. Endocrinol., 12.","DOI":"10.3389\/fendo.2021.688654"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Jin, H., Shen, X., Song, W., Liu, Y., Qi, L., and Zhang, F. (2021). The Development of Nomograms to Predict Blastulation Rate Following Cycles of In Vitro Fertilization in Patients With Tubal Factor Infertility, Polycystic Ovary Syndrome, or Endometriosis. Front. Endocrinol., 12.","DOI":"10.3389\/fendo.2021.751373"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1093\/humrep\/der392","article-title":"Nomogram to Predict Pregnancy Rate after ICSI-IVF Cycle in Patients with Endometriosis","volume":"27","author":"Ballester","year":"2012","journal-title":"Hum. Reprod. Oxf. Engl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.1080\/14647273.2023.2285937","article-title":"Predictive Models for Starting Dose of Gonadotropin in Controlled Ovarian Hyperstimulation: Review and Progress Update","volume":"26","author":"Guo","year":"2023","journal-title":"Hum. Fertil. Camb. Engl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"AlSaad, R., Abd-Alrazaq, A., Choucair, F., Ahmed, A., Aziz, S., and Sheikh, J. (2024). Harnessing Artificial Intelligence to Predict Ovarian Stimulation Outcomes in In Vitro Fertilization: Scoping Review. J. Med. Internet Res., 26.","DOI":"10.2196\/53396"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1918","DOI":"10.1093\/humrep\/dead163","article-title":"Predicting the Number of Oocytes Retrieved from Controlled Ovarian Hyperstimulation with Machine Learning","volume":"38","author":"Ferrand","year":"2023","journal-title":"Hum. Reprod. Oxf. Engl."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ejogrb.2023.03.022","article-title":"Prediction of Oocyte Maturation Rate in the GnRH Antagonist Flexible IVF Protocol Using a Novel Machine Learning Algorithm\u2014A Retrospective Study","volume":"284","author":"Houri","year":"2023","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1093\/biolre\/ioac219","article-title":"Oocyte Quality Evaluation: A Review of Engineering Approaches toward Clinical Challenges","volume":"108","author":"Palay","year":"2023","journal-title":"Biol. Reprod."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10815-022-02707-6","article-title":"The Use of Voting Ensembles to Improve the Accuracy of Deep Neural Networks as a Non-Invasive Method to Predict Embryo Ploidy Status","volume":"40","author":"Jiang","year":"2023","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Correa, N., Cerquides, J., Arcos, J.L., Vassena, R., and Popovic, M. (2024). Personalizing the First Dose of FSH for IVF\/ICSI Patients through Machine Learning: A Non-Inferiority Study Protocol for a Multi-Center Randomized Controlled Trial. Trials, 25.","DOI":"10.1186\/s13063-024-07907-2"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, K., Gao, Y., Chen, L., Gao, T., Ma, Y., Jiang, Z., Yang, G., Feng, F., and Zhang, S. (2024). A Generalized AI System for Human Embryo Selection Covering the Entire IVF Cycle via Multi-Modal Contrastive Learning. Patterns, 5.","DOI":"10.1016\/j.patter.2024.100985"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.rbmo.2020.09.031","article-title":"An Artificial Intelligence Model Based on the Proteomic Profile of Euploid Embryos and Blastocyst Morphology: A Preliminary Study","volume":"42","author":"Bori","year":"2021","journal-title":"Reprod. Biomed. Online"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"901","DOI":"10.1007\/s10815-023-02745-8","article-title":"Deep Learning Analysis of Endometrial Histology as a Promising Tool to Predict the Chance of Pregnancy after Frozen Embryo Transfers","volume":"40","author":"Li","year":"2023","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1093\/humrep\/dead254","article-title":"A Brief History of Artificial Intelligence Embryo Selection: From Black-Box to Glass-Box","volume":"39","author":"Lee","year":"2024","journal-title":"Hum. Reprod. Oxf. Engl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.fertnstert.2023.06.025","article-title":"Noninvasive Genetic Screening: Current Advances in Artificial Intelligence for Embryo Ploidy Prediction","volume":"120","author":"Jiang","year":"2023","journal-title":"Fertil. Steril."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Hall, J.M.M., Nguyen, T.V., Dinsmore, A.W., Perugini, D., Perugini, M., Fukunaga, N., Asada, Y., Schiewe, M., Lim, A.Y.X., and Lee, C. (2024). Use of Federated Learning to Develop an Artificial Intelligence Model Predicting Usable Blastocyst Formation from Pre-ICSI Oocyte Images. Reprod. Biomed. Online, 49.","DOI":"10.1016\/j.rbmo.2024.104403"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Liao, Q., Zhang, Q., Feng, X., Huang, H., Xu, H., Tian, B., Liu, J., Yu, Q., Guo, N., and Liu, Q. (2021). Development of Deep Learning Algorithms for Predicting Blastocyst Formation and Quality by Time-Lapse Monitoring. Commun. Biol., 4.","DOI":"10.1038\/s42003-021-01937-1"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Fjeldstad, J., Qi, W., Siddique, N., Mercuri, N., Nayot, D., and Krivoi, A. (2024). Segmentation of Mature Human Oocytes Provides Interpretable and Improved Blastocyst Outcome Predictions by a Machine Learning Model. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-60901-1"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Rajendran, S., Brendel, M., Barnes, J., Zhan, Q., Malmsten, J.E., Zisimopoulos, P., Sigaras, A., Ofori-Atta, K., Meseguer, M., and Miller, K.A. (2024). Automatic Ploidy Prediction and Quality Assessment of Human Blastocysts Using Time-Lapse Imaging. Nat. Commun., 15.","DOI":"10.1038\/s41467-024-51823-7"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Li, X., Yao, Y., Zhao, D., Chang, X., Li, Y., Lin, H., Wei, H., Wang, H., Mi, Y., and Huang, L. (2024). Clinical Outcomes of Single Blastocyst Transfer with Machine Learning Guided Noninvasive Chromosome Screening Grading System in Infertile Patients. Reprod. Biol. Endocrinol. RBE, 22.","DOI":"10.1186\/s12958-024-01231-9"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Jin, L., Si, K., Li, Z., He, H., Wu, L., Ma, B., Ren, X., and Huang, B. (2024). Multiple Collapses of Blastocysts after Full Blastocyst Formation Is an Independent Risk Factor for Aneuploidy\u2014A Study Based on AI and Manual Validation. Reprod. Biol. Endocrinol. RBE, 22.","DOI":"10.1186\/s12958-024-01242-6"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s10815-022-02562-5","article-title":"Correlation between an Annotation-Free Embryo Scoring System Based on Deep Learning and Live Birth\/Neonatal Outcomes after Single Vitrified-Warmed Blastocyst Transfer: A Single-Centre, Large-Cohort Retrospective Study","volume":"39","author":"Ueno","year":"2022","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1097\/CM9.0000000000002803","article-title":"Knowledge-Embedded Spatio-Temporal Analysis for Euploidy Embryos Identification in Couples with Chromosomal Rearrangements","volume":"137","author":"Chen","year":"2024","journal-title":"Chin. Med. J."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Salih, M., Austin, C., Warty, R.R., Tiktin, C., Rolnik, D.L., Momeni, M., Rezatofighi, H., Reddy, S., Smith, V., and Vollenhoven, B. (2023). Embryo Selection through Artificial Intelligence versus Embryologists: A Systematic Review. Hum. Reprod. Open, 2023.","DOI":"10.1093\/hropen\/hoad031"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Fjeldstad, J., Qi, W., Mercuri, N., Siddique, N., Meriano, J., Krivoi, A., and Nayot, D. (2024). An Artificial Intelligence Tool Predicts Blastocyst Development from Static Images of Fresh Mature Oocytes. Reprod. Biomed. Online, 48.","DOI":"10.1016\/j.rbmo.2024.103842"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1016\/j.fertnstert.2024.01.005","article-title":"Diagnostic or Prognostic? Decoding the Role of Embryo Selection on in Vitro Fertilization Treatment Outcomes","volume":"121","author":"Zou","year":"2024","journal-title":"Fertil. Steril."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1007\/s00404-024-07479-1","article-title":"Exploring the Potential of Machine Learning in Gynecological Care: A Review","volume":"309","author":"Khan","year":"2024","journal-title":"Arch. Gynecol. Obstet."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"GhoshRoy, D., Alvi, P.A., and Santosh, K.C. (2023). AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review. J. Med. Syst., 47.","DOI":"10.1007\/s10916-023-01983-8"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"de Santiago, I., and Polanski, L. (2022). Data-Driven Medicine in the Diagnosis and Treatment of Infertility. J. Clin. Med., 11.","DOI":"10.3390\/jcm11216426"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"2817","DOI":"10.1007\/s10815-020-01950-z","article-title":"Fertility AI Forum Group AI in the Treatment of Fertility: Key Considerations","volume":"37","author":"Swain","year":"2020","journal-title":"J. Assist. Reprod. Genet."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ahmed, M.I., Spooner, B., Isherwood, J., Lane, M., Orrock, E., and Dennison, A. (2023). A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare. Cureus, 15.","DOI":"10.7759\/cureus.46454"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Gbagbo, F.Y., Ameyaw, E.K., and Yaya, S. (2024). Artificial Intelligence and Sexual Reproductive Health and Rights: A Technological Leap towards Achieving Sustainable Development Goal Target 3.7. Reprod. Health, 21.","DOI":"10.1186\/s12978-024-01924-9"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.fertnstert.2023.05.152","article-title":"Unlock the Algorithms: Regulation of Adaptive Algorithms in Reproduction","volume":"120","author":"Curchoe","year":"2023","journal-title":"Fertil. Steril."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., and King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med., 17.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Mendizabal-Ruiz, G., Paredes, O., \u00c1lvarez, \u00c1., Acosta-G\u00f3mez, F., Hern\u00e1ndez-Morales, E., Gonz\u00e1lez-Sandoval, J., Mendez-Zavala, C., Borrayo, E., and Chavez-Badiola, A. (2024). Artificial Intelligence in Human Reproduction. Arch. Med. Res., 55.","DOI":"10.1016\/j.arcmed.2024.103131"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Kharb, S., and Joshi, A. (2023). Multi-Omics and Machine Learning for the Prevention and Management of Female Reproductive Health. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1081667"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1093\/biolre\/ioac109","article-title":"Multiomics Analysis of Male Infertility","volume":"107","author":"Wu","year":"2022","journal-title":"Biol. Reprod."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ramakrishnan, R., Rao, S., and He, J.-R. (2021). Perinatal Health Predictors Using Artificial Intelligence: A Review. Womens Health Lond. Engl., 17.","DOI":"10.1177\/17455065211046132"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Mohsin Khan, M., Shah, N., Shaikh, N., Thabet, A., Alrabayah, T., and Belkhair, S. (2024). Towards Secure and Trusted AI in Healthcare: A Systematic Review of Emerging Innovations and Ethical Challenges. Int. J. Med. 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