{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T09:46:05Z","timestamp":1775900765229,"version":"3.50.1"},"reference-count":107,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"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>Artificial intelligence (AI) is the new medical hot topic, being applied mainly in specialties with a strong imaging component. In the domain of gynecology, AI has been tested and shown vast potential in several areas with promising results, with an emphasis on oncology. However, fewer studies have been made focusing on urogynecology, a branch of gynecology known for using multiple imaging exams (IEs) and tests in the management of women\u2019s pelvic floor health. This review aims to illustrate the current state of AI in urogynecology, namely with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.<\/jats:p>","DOI":"10.3390\/diagnostics15030274","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T04:32:22Z","timestamp":1737693142000},"page":"274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Role of Artificial Intelligence in Urogynecology: Current Applications and Future Prospects"],"prefix":"10.3390","volume":"15","author":[{"given":"Maria Beatriz Macedo de","family":"Oliveira","sequence":"first","affiliation":[{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5890-7049","authenticated-orcid":false,"given":"Francisco","family":"Mendes","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"}]},{"given":"Miguel","family":"Martins","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9427-5635","authenticated-orcid":false,"given":"Pedro","family":"Cardoso","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0887-8796","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Fonseca","sequence":"additional","affiliation":[{"name":"CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, 4200-427 Porto, Portugal"}]},{"given":"Teresa","family":"Mascarenhas","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Department of Obstetrics and Gynecology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0340-0830","authenticated-orcid":false,"given":"Miguel Mascarenhas","family":"Saraiva","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, University of Porto, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"Department of Gastroenterology, S\u00e3o Jo\u00e3o University Hospital, Alameda Professor Hern\u00e2ni Monteiro, 4200-427 Porto, Portugal"},{"name":"WGO Gastroenterology and Hepatology Training Center, 4200-427 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.4103\/jfmpc.jfmpc_440_19","article-title":"Overview of artificial intelligence in medicine","volume":"8","author":"Amisha","year":"2019","journal-title":"J. Fam. Med. Prim. Care"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mota, J., Almeida, M.J., Mendes, F., Martins, M., Ribeiro, T., Afonso, J., Cardoso, P., Cardoso, H., Andrade, P., and Ferreira, J. (2024). From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?. Diagnostics, 14.","DOI":"10.3390\/diagnostics14030291"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Brand\u00e3o, M., Mendes, F., Martins, M., Cardoso, P., Macedo, G., Mascarenhas, T., and Saraiva, M.M. (2024). Revolutionizing Women\u2019s Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology. J. Clin. Med., 13.","DOI":"10.3390\/jcm13041061"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"365","DOI":"10.4103\/ijd.IJD_421_20","article-title":"Artificial Intelligence: How is It Changing Medical Sciences and Its Future?","volume":"65","author":"Basu","year":"2020","journal-title":"Indian J. Dermatol."},{"key":"ref_5","first-page":"e7124","article-title":"Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice","volume":"12","author":"Iftikhar","year":"2020","journal-title":"Cureus"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Alenco\u00e3o, I., Carinhas, M.J., Martins, M., Cardoso, P., Mendes, F., Fernandes, J., Ferreira, J., Macedo, G., and Zulmira Macedo, R. (2024). Artificial Intelligence and Colposcopy: Automatic Identification of Cervical Squamous Cell Carcinoma Precursors. J. Clin. Med., 13.","DOI":"10.3390\/jcm13103003"},{"key":"ref_8","first-page":"1","article-title":"The Use of Artificial Intelligence in Urogynecology","volume":"12","author":"Khaki","year":"2023","journal-title":"Int. J. Women\u2019s Health Reprod. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ygyno.2022.07.024","article-title":"A systematic review on the use of artificial intelligence in gynecologic imaging\u2014Background, state of the art, and future directions","volume":"166","author":"Shrestha","year":"2022","journal-title":"Gynecol. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","article-title":"Computing machinery and intelligence","volume":"59","author":"Turing","year":"1950","journal-title":"Mind"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"118221","DOI":"10.1016\/j.eswa.2022.118221","article-title":"Tracing the evolution of AI in the past decade and forecasting the emerging trends","volume":"209","author":"Shao","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial Intelligence in healthcare: Past, Present and Future","volume":"2","author":"Jiang","year":"2017","journal-title":"Stroke Vasc. Neurol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: Trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6679512","DOI":"10.1155\/2021\/6679512","article-title":"Involvement of Machine Learning Tools in Healthcare Decision Making","volume":"2021","author":"Jayatilake","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1007\/s00192-023-05612-3","article-title":"The role of artificial intelligence in the future of urogynecology","volume":"34","author":"Daykan","year":"2023","journal-title":"Int. Urogynecol. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Seval, M.M., and Varl\u0131, B. (2023). Current developments in artificial intelligence from obstetrics and gynecology to urogynecology. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1098205"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Girmay, S., Samsom, F., and Khattak, A.M. (2021, January 12\u201314). AI based Login System using Facial Recognition. Proceedings of the 5th Cyber Security in Networking Conference (CSNet), Abu Dhabi, United Arab Emirates. Available online: https:\/\/ieeexplore.ieee.org\/document\/9614281.","DOI":"10.1109\/CSNet52717.2021.9614281"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1053\/j.gastro.2019.08.058","article-title":"Application of artificial intelligence to gastroenterology and hepatology","volume":"158","author":"Sandborn","year":"2020","journal-title":"Gastroenterology"},{"key":"ref_19","first-page":"300","article-title":"Artificial intelligence and capsule endoscopy: Unravelling the future","volume":"34","author":"Mascarenhas","year":"2021","journal-title":"Ann. Gastroenterol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"RadioGraphics"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: Machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J. Intern. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2021","DOI":"10.1007\/s00330-019-06564-3","article-title":"Deep learning: Definition and perspectives for thoracic imaging","volume":"30","author":"Chassagnon","year":"2020","journal-title":"Eur. Radiol."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1007\/s10620-021-07086-z","article-title":"Artificial Intelligence in Endoscopy","volume":"67","author":"Okagawa","year":"2022","journal-title":"Dig. Dis. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neunet.2016.12.002","article-title":"Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection","volume":"87","author":"Kim","year":"2017","journal-title":"Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat. Rev. Cancer"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.vgie.2020.08.013","article-title":"Artificial intelligence in gastrointestinal endoscopy","volume":"5","author":"Pannala","year":"2020","journal-title":"VideoGIE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/2374289519873088","article-title":"Artificial intelligence and machine learning in pathology:the present landscape of supervised methods","volume":"6","author":"Rashidi","year":"2019","journal-title":"Acad. Pathol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e10493","DOI":"10.2196\/10493","article-title":"Unsupervised machine learning to identify high likelihood of dementia in population-based surveys: Development and validation study","volume":"20","author":"Bayen","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","article-title":"Artificial Intelligence in Surgery","volume":"268","author":"Hashimoto","year":"2018","journal-title":"Ann. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1109\/JBHI.2014.2361688","article-title":"Support Vector Feature Selection for Early Detection of Anastomosis Leakage From Bag-of-Words in Electronic Health Records","volume":"20","author":"Hindberg","year":"2016","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.tacc.2021.02.007","article-title":"Natural language processing in medicine: A review","volume":"38","author":"Locke","year":"2021","journal-title":"Trends Anaesth. Crit. Care"},{"key":"ref_33","unstructured":"Angelica, C., Purnama, H., and Purnomo, F. (2021, January 28). Impact of Computer Vision With Deep Learning Approach in Medical Imaging Diagnosis. Proceedings of the International Conference on Computer Science and Artificial Intelligence (ICCSAI), Jakarta, Indonesia."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3830","DOI":"10.21037\/qims-20-1151","article-title":"What is new in computer vision and artificial intelligence in medical image analysis applications","volume":"11","author":"Olveres","year":"2021","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1016\/j.surg.2020.10.039","article-title":"Computer vision in surgery","volume":"169","author":"Ward","year":"2021","journal-title":"Surgery"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"A1","DOI":"10.1016\/j.mri.2019.12.006","article-title":"Artificial intelligence in medical imaging","volume":"68","author":"Gore","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_37","unstructured":"Yoon, I., and Gupta, N. (2023). Pelvic prolapse imaging. StatPearls, StatPearls Publishing."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109717","DOI":"10.1016\/j.ejrad.2021.109717","article-title":"Artificial intelligence in ultrasound","volume":"139","author":"Shen","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1053\/j.semnuclmed.2020.10.001","article-title":"Artificial Intelligence for Optimization and Interpretation of PET\/CT and PET\/MR Images","volume":"51","author":"Zaharchuk","year":"2021","journal-title":"Semin. Nucl. Med."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Paudyal, R., Shah, A.D., Akin, O., Do, R.K.G., Konar, A.S., Hatzoglou, V., Mahmood, U., Lee, N., Wong, R.J., and Banerjee, S. (2023). Artificial Intelligence in CT and MR Imaging for Oncological Applications. Cancers, 15.","DOI":"10.3390\/cancers15092573"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1016\/j.clindermatol.2024.06.020","article-title":"Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans","volume":"42","author":"Shapiro","year":"2024","journal-title":"Clin. Dermatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1002\/nau.24578","article-title":"Pattern recognition algorithm to identify detrusor overactivity on urodynamics","volume":"40","author":"Wang","year":"2021","journal-title":"Neurourol. Urodyn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.urology.2021.09.027","article-title":"Machine Learning for Urodynamic Detection of Detrusor Overactivity","volume":"159","author":"Hobbs","year":"2022","journal-title":"Urology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1007\/s00192-013-2287-4","article-title":"Assessment of a semiautomated pelvic floor measurement model for evaluating pelvic organ prolapse on MRI","volume":"25","author":"Onal","year":"2014","journal-title":"Int. Urogynecol. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"014008","DOI":"10.1117\/1.JMI.5.1.014008","article-title":"Automated contour tracking and trajectory classification of pelvic organs on dynamic MRI","volume":"5","author":"Nekooeimehr","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2869","DOI":"10.1007\/s00192-021-05064-7","article-title":"Multi-label classification of pelvic organ prolapse using stress magnetic resonance imaging with deep learning","volume":"33","author":"Wang","year":"2022","journal-title":"Int. Urogynecol. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1007\/s00192-021-04859-y","article-title":"Artificial intelligence models derived from 2D transperineal ultrasound images in the clinical diagnosis of stress urinary incontinence","volume":"33","author":"Zhang","year":"2021","journal-title":"Int. Urogynecol. J."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"6215","DOI":"10.1002\/mp.16389","article-title":"Automated segmentation and measurement of the female pelvic floor from the mid-sagittal plane of 3D ultrasound volumes","volume":"50","author":"Szentimrey","year":"2023","journal-title":"Med. Phys."},{"key":"ref_49","first-page":"e2024.00007","article-title":"Current Status and Role of Artificial Intelligence in Anorectal Diseases and Pelvic Floor Disorders","volume":"28","author":"Aleissa","year":"2024","journal-title":"JSLS J. Soc. Laparosc. Robot. Surg."},{"key":"ref_50","first-page":"1","article-title":"Evaluation of Nursing Effect of Pelvic Floor Rehabilitation Training on Pelvic Organ Prolapse in Postpartum Pregnant Women under Ultrasound Imaging with Artificial Intelligence Algorithm","volume":"2022","author":"Yin","year":"2022","journal-title":"Comput. Math. Methods Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2349","DOI":"10.1007\/s00345-019-03059-0","article-title":"Application of artificial neural networks for automated analysis of cystoscopic images: A review of the current status and future prospects","volume":"38","author":"Negassi","year":"2020","journal-title":"World J. Urol."},{"key":"ref_52","first-page":"742","article-title":"Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach","volume":"42","author":"Cabeza","year":"2015","journal-title":"Ultrasound Med. Biol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9","DOI":"10.31491\/UTJ.2024.03.018","article-title":"Artificial Intelligence and Augmented Reality: Transforming Urology?","volume":"8","author":"Licari","year":"2024","journal-title":"Uro-Technol. J."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Taylor, R.A., Moore, C.L., Cheung, K.H., and Brandt, C. (2018). Predicting urinary tract infections in the emergency department with machine learning. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0194085"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"119854","DOI":"10.1016\/j.cca.2024.119854","article-title":"An artificial intelligence-driven support tool for prediction of urine culture test results","volume":"562","author":"Dedeene","year":"2024","journal-title":"Clin. Chim. Acta"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Burton, R.J., Albur, M., Eberl, M., and Cuff, S.M. (2019). Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections. BMC Med. Inform. Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0878-9"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Go\u017adzikiewicz, N., Zwoli\u0144ska, D., and Polak-Jonkisz, D. (2022). The Use of Artificial Intelligence Algorithms in the Diagnosis of Urinary Tract Infections\u2014A Literature Review. J. Clin. Med., 11.","DOI":"10.3390\/jcm11102734"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"znab259.947","DOI":"10.1093\/bjs\/znab259.947","article-title":"1109 Artificial Intelligence in Urological Oncology","volume":"108","author":"Brodie","year":"2021","journal-title":"Br. J. Surg."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e23483","DOI":"10.2196\/23483","article-title":"Artificial intelligence techniques that may be applied to primary care data to facilitate the earlier diagnosis of cancer: A systematic review (Preprint)","volume":"23","author":"Jones","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41746-022-00707-5","article-title":"Computer vision in surgery: From potential to clinical value","volume":"5","author":"Mascagni","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41746-021-00423-6","article-title":"The potential of artificial intelligence to improve patient safety: A scoping review","volume":"4","author":"Bates","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1097\/AOG.0000000000001425","article-title":"Prevention, Recognition, and Management of Urologic Injuries During Gynecologic Surgery","volume":"127","author":"Sharp","year":"2016","journal-title":"Obstet. Gynecol."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Serban, N., Kupas, D., Hajdu, A., T\u00f6r\u00f6k, P., and Harangi, B. (2024). Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks. Sensors, 24.","DOI":"10.3390\/s24092926"},{"key":"ref_64","first-page":"e1596","article-title":"Deep Learning-Based Real-time Ureter Identification in Laparoscopic Colorectal Surgery","volume":"67","author":"Narihiro","year":"2024","journal-title":"Dis. Colon Rectum"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1093\/bjs\/znad249","article-title":"Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery","volume":"110","author":"Kitaguchi","year":"2023","journal-title":"Br. J. Surg."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5702","DOI":"10.1177\/00031348231173981","article-title":"Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning","volume":"89","author":"Chen","year":"2023","journal-title":"Am. Surg."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1109\/ACCESS.2018.2889138","article-title":"Laparoscopic Image-Guided System Based on Multispectral Imaging for the Ureter Detection","volume":"7","author":"Yu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"27S","DOI":"10.1097\/AOG.0000000000002860","article-title":"An Interactive Holographic Curriculum for Urogynecologic Surgery","volume":"132","author":"Siff","year":"2018","journal-title":"Obstet. Gynecol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"78","DOI":"10.9734\/cjast\/2021\/v40i631320","article-title":"Application of Artificial Intelligence in Healthcare: Chances and Challenges","volume":"40","author":"Manne","year":"2021","journal-title":"Curr. J. Appl. Sci. Technol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1097\/AOG.0000000000003517","article-title":"Development and Validation of a Machine Learning Algorithm for Predicting Response to Anticholinergic Medications for Overactive Bladder Syndrome","volume":"134","author":"Sheyn","year":"2019","journal-title":"Obstet. Gynecol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1007\/s00192-020-04243-2","article-title":"Innovative use of artificial intelligence in urogynecology","volume":"31","author":"Bentaleb","year":"2020","journal-title":"Int. Urogynecol. J."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"125626","DOI":"10.1109\/ACCESS.2022.3221816","article-title":"Bladder Monitoring Systems: State of The Art and Future Perspectives","volume":"10","author":"Semproni","year":"2022","journal-title":"IEEE Access"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1007\/s11517-018-1942-9","article-title":"Feasibility study of intelligent autonomous determination of the bladder voiding need to treat bedwetting using ultrasound and smartphone ML techniques","volume":"57","author":"Kuru","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"280","DOI":"10.5213\/inj.2346338.169","article-title":"Artificial Intelligence-Based Patient Monitoring System for Medical Support","volume":"27","author":"Kim","year":"2023","journal-title":"Int. Neurourol. J."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"229","DOI":"10.5213\/inj.2142276.138","article-title":"Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device","volume":"25","author":"Eun","year":"2021","journal-title":"Int. Neurourol. J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1097\/SPV.0000000000000580","article-title":"In Search of Mobile Applications for Urogynecology Providers","volume":"25","author":"Wallace","year":"2019","journal-title":"Female Pelvic Med. Reconstr. Surg."},{"key":"ref_77","first-page":"877","article-title":"Self-management of incontinence using a free mobile app: Factors associated with improvement","volume":"33","author":"Samuelsson","year":"2021","journal-title":"Int. Urogynecol. J."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1089\/tmj.2017.0316","article-title":"Evaluation of a Mobile Application for Pelvic Floor Exercises","volume":"25","author":"Han","year":"2019","journal-title":"Telemed. e-Health"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Martins, M., Ribeiro, T., Afonso, J., Cardoso, P., Mendes, F., Cardoso, H., Almeida, R., Ferreira, J., and Fonseca, J. (2024). Software as a Medical Device (SaMD) in Digestive Healthcare: Regulatory Challenges and Ethical Implications. Diagnostics, 14.","DOI":"10.3390\/diagnostics14182100"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.chb.2014.04.043","article-title":"It\u2019s only a computer: Virtual humans increase willingness to disclose","volume":"37","author":"Lucas","year":"2014","journal-title":"Comput. Hum. Behav."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1007\/s00508-011-0013-0","article-title":"The greatest taboo: Urinary incontinence as a source of shame and embarrassment","volume":"123","author":"Elenskaia","year":"2011","journal-title":"Wien. Klin. Wochenschr."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1817","DOI":"10.1002\/nau.25557","article-title":"\u201cThere is a lot of shame that comes with this\u201d: A qualitative study of patient experiences of isolation, embarrassment, and stigma associated with overactive bladder","volume":"43","author":"Brodsky","year":"2024","journal-title":"Neurourol. Urodyn."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Trebeschi, S., Bodalal, Z., van Dijk, N., Boellaard, T.N., Apfaltrer, P., Bucho, T.M.T., Nguyen-Kim, T.D.L., van der Heijden, M.S., Aerts, H.J.W.L., and Beets-Tan, R.G.H. (2021). Development of a Prognostic AI-Monitor for Metastatic Urothelial Cancer Patients Receiving Immunotherapy. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.637804"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"e18599","DOI":"10.2196\/18599","article-title":"Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review","volume":"8","author":"Choudhury","year":"2020","journal-title":"JMIR Med. Inform."},{"key":"ref_85","first-page":"1","article-title":"AI Anxiety: A Comprehensive Analysis of Psychological Factors and Interventions","volume":"2023","author":"Kim","year":"2023","journal-title":"SSRN Electron. J."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Mascarenhas, M., Afonso, J., Ribeiro, T., Andrade, P., Cardoso, H., and Macedo, G. (2023). The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. Medicina, 59.","DOI":"10.3390\/medicina59040790"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, B., Asan, O., and Mansouri, M. (2022, January 24\u201326). Patients\u2019 Perceptions of Integrating AI into Healthcare: Systems Thinking Approach. Proceedings of the 2022 IEEE International Symposium on Systems Engineering (ISSE), Vienna, Austria. Available online: https:\/\/ieeexplore.ieee.org\/document\/10005383.","DOI":"10.1109\/ISSE54508.2022.10005383"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1038\/s41746-019-0132-y","article-title":"Patients\u2019 Views of Wearable Devices and AI in healthcare: Findings from the ComPaRe e-cohort","volume":"2","author":"Tran","year":"2019","journal-title":"NPJ Digit. Med."},{"key":"ref_89","first-page":"1","article-title":"Medicine and engineering collaboration in urogynecology: A narrative review","volume":"5","author":"Hu","year":"2021","journal-title":"Gynecol. Pelvic Med."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1200\/CCI.19.00079","article-title":"Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center","volume":"4","author":"Beck","year":"2020","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1007\/s12553-023-00738-2","article-title":"Artificial Intelligence Applied to clinical trials: Opportunities and challenges","volume":"13","author":"Askin","year":"2023","journal-title":"Health Technol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"e080032","DOI":"10.1136\/bmjopen-2023-080032","article-title":"Artificial intelligence tools for optimising recruitment and retention in clinical trials: A scoping review protocol","volume":"14","author":"Lu","year":"2024","journal-title":"BMJ Open"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.1093\/jamia\/ocae243","article-title":"Artificial intelligence for optimizing recruitment and retention in clinical trials: A scoping review","volume":"31","author":"Lu","year":"2024","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_94","first-page":"60","article-title":"Optimizing Drug Discovery and Development with AI-Powered Clinical Trials Management: Applies AI algorithms to optimize the design and execution of clinical trials, accelerating drug discovery and development processes in the pharmaceutical industry","volume":"4","author":"Kowalski","year":"2024","journal-title":"J. Mach. Learn. Healthc. Decis. Support"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1053\/j.semnuclmed.2020.09.003","article-title":"How to Design AI-Driven Clinical Trials in Nuclear Medicine","volume":"51","author":"Delso","year":"2021","journal-title":"Semin. Nucl. Med."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1039\/D3DD00185G","article-title":"Machine Learning for Hypothesis Generation in Biology and Medicine: Exploring the latent space of neuroscience and developmental bioelectricity","volume":"3","author":"Stremmel","year":"2024","journal-title":"Digit. Discov."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Xianyu, Z., Correia, C., Yong Ung, C., Zhu, S., Billadeau, D.D., and Li, H. (2024). The Rise of Hypothesis-Driven Artificial Intelligence in Oncology. Cancers, 16.","DOI":"10.3390\/cancers16040822"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"e16048","DOI":"10.2196\/16048","article-title":"Introducing Artificial Intelligence Training in Medical Education (Preprint)","volume":"5","author":"Paranjape","year":"2019","journal-title":"JMIR Med. Educ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e31043","DOI":"10.2196\/31043","article-title":"Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review","volume":"7","author":"Charow","year":"2021","journal-title":"JMIR Med. Educ."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e43333","DOI":"10.2196\/43333","article-title":"Digital Education for the Deployment of Artificial Intelligence in Health Care","volume":"25","author":"Malerbi","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_101","first-page":"133","article-title":"Application of Artificial Intelligence in Medical Education: Current Scenario and Future Perspectives","volume":"11","author":"Mir","year":"2023","journal-title":"J. Adv. Med. Educ. Prof."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e19285","DOI":"10.2196\/19285","article-title":"Artificial Intelligence Education and Tools for Medical and Health Informatics Students: Systematic Review","volume":"6","author":"Sapci","year":"2020","journal-title":"JMIR Med. Educ."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"145248","DOI":"10.1109\/ACCESS.2021.3118960","article-title":"Smart Healthcare in the Age of AI: Recent Advances, Challenges, and Future Prospects","volume":"9","author":"Nasr","year":"2021","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Chomutare, T., Tejedor, M., Svenning, T.O., Marco-Ruiz, L., Tayefi, M., Lind, K., Godtliebsen, F., Moen, A., Ismail, L., and Makhlysheva, A. (2022). Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph192316359"},{"key":"ref_105","unstructured":"Madiega, T. (2024, October 22). Artificial Intelligence Act. Available online: https:\/\/www.europarl.europa.eu\/RegData\/etudes\/BRIE\/2021\/698792\/EPRS_BRI(2021)698792_EN.pdf."},{"key":"ref_106","unstructured":"Manning, C. (2024, October 22). Technology Readiness Levels; NASA, Available online: https:\/\/www.nasa.gov\/directorates\/somd\/space-communications-navigation-program\/technology-readiness-levels\/."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Liu, X., Shi, J., Li, Z., Huang, Y., Zhang, Z., and Zhang, C. (2023). The Present and Future of Artificial Intelligence in Urological Cancer. J. Clin. Med., 12.","DOI":"10.3390\/jcm12154995"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/3\/274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:17Z","timestamp":1759919717000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/3\/274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":107,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["diagnostics15030274"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15030274","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}