{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T06:28:30Z","timestamp":1792477710017,"version":"4.5.0"},"reference-count":93,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:00:00Z","timestamp":1742688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,4,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Introduction<\/jats:title>\n                  <jats:p>Artificial Intelligence (AI) has witnessed significant growth in the field of medicine, leveraging machine learning, artificial neuron networks, and large language models. These technologies are effective in disease diagnosis, education, and prevention, while raising ethical concerns and potential challenges. However, their utility in sexual medicine remains relatively unexplored.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>We aim to provide a comprehensive summary of the status of AI in the field of sexual medicine.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>A comprehensive search was conducted using MeSH keywords, including \"artificial intelligence,\" \"sexual medicine,\" \"sexual health,\" and \"machine learning.\" Two investigators screened articles for eligibility within the PubMed and MEDLINE databases, with conflicts resolved by a third reviewer. Articles in English language that reported on AI in sexual medicine and health were included. A total of 69 full-text articles were systematically analyzed based on predefined inclusion criteria. Data extraction included information on article characteristics, study design, assessment methods, and outcomes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The initial search yielded 905 articles relevant to AI in sexual medicine. Upon assessing the full texts of 121 articles for eligibility, 52 studies unrelated to AI in sexual health were excluded, resulting in 69 articles for systematic review. The analysis revealed AI's accuracy in preventing, diagnosing, and decision-making in sexually transmitted diseases. AI also demonstrated the ability to diagnose and offer precise treatment plans for male and female sexual dysfunction and infertility, accurately predict sex from bone and teeth imaging, and correctly predict and diagnose sexual orientation and relationship issues. AI emerged as a promising modality with significant implications for the future of sexual medicine.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Further research is essential to unlock the potential of AI in sexual medicine. AI presents advantages such as accessibility, user-friendliness, confidentiality, and a preferred source of sexual health information. However, it still lags human healthcare providers in terms of compassion and clinical expertise.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/sxmrev\/qeaf009","type":"journal-article","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T12:17:26Z","timestamp":1740831446000},"page":"267-279","source":"Crossref","is-referenced-by-count":14,"title":["Sexual health in the era of artificial intelligence: a scoping review of the literature"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0757-896X","authenticated-orcid":false,"given":"Elia","family":"Abou Chawareb","sequence":"first","affiliation":[{"name":"Department of Urology, University of California, Irvine , 92697, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian H","family":"Im","sequence":"additional","affiliation":[{"name":"Department of Urology, Sidney Kimmel Medical College, Thomas Jefferson University , Philadelphia, PA 19107 ,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sherry","family":"Lu","sequence":"additional","affiliation":[{"name":"Chicago Medical School, Rosalind Franklin University of Medicine and Science , Chicago, 60064, IL, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5396-1364","authenticated-orcid":false,"given":"Muhammed A M","family":"Hammad","sequence":"additional","affiliation":[{"name":"Department of Urology, University of California, Irvine , 92697, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiffany R","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Urology, University of California, Irvine , 92697, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Osteopathic Medicine, A.T. Still University , San Diego, 92123, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faysal A","family":"Yafi","sequence":"additional","affiliation":[{"name":"Department of Urology, University of California, Irvine , 92697, CA, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,3,23]]},"reference":[{"issue":"2","key":"2025041607173293600_ref1","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3390\/a15020071","article-title":"A review of deep learning algorithms and their applications in healthcare","volume":"15","author":"Abdel-Jaber","year":"2022","journal-title":"Algorithms"},{"issue":"4","key":"2025041607173293600_ref2","doi-asserted-by":"publisher","first-page":"e217249","DOI":"10.1001\/jamanetworkopen.2021.7249","article-title":"Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in Teledermatology practices","volume":"4","author":"Jain","year":"2021","journal-title":"JAMA Netw Open"},{"issue":"22","key":"2025041607173293600_ref3","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"issue":"8","key":"2025041607173293600_ref4","doi-asserted-by":"publisher","first-page":"1264","DOI":"10.1016\/j.ophtha.2018.01.034","article-title":"Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy","volume":"125","author":"Krause","year":"2018","journal-title":"Ophthalmology"},{"issue":"1","key":"2025041607173293600_ref5","doi-asserted-by":"publisher","first-page":"e10","DOI":"10.1016\/S2589-7500(20)30250-8","article-title":"Predicting the risk of developing diabetic retinopathy using deep learning","volume":"3","author":"Bora","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"6","key":"2025041607173293600_ref6","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","article-title":"End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography","volume":"25","author":"Ardila","year":"2019","journal-title":"Nat Med"},{"issue":"9","key":"2025041607173293600_ref7","doi-asserted-by":"publisher","first-page":"1372","DOI":"10.1001\/jamaoncol.2020.2485","article-title":"Development and validation of a deep learning algorithm for Gleason grading of prostate cancer from biopsy specimens","volume":"6","author":"Nagpal","year":"2020","journal-title":"JAMA Oncol"},{"issue":"11","key":"2025041607173293600_ref8","doi-asserted-by":"publisher","first-page":"e2023267","DOI":"10.1001\/jamanetworkopen.2020.23267","article-title":"Evaluation of the use of combined artificial intelligence and pathologist assessment to review and grade prostate biopsies","volume":"3","author":"Steiner","year":"2020","journal-title":"JAMA Netw Open"},{"issue":"1","key":"2025041607173293600_ref9","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1038\/s43856-021-00005-3","article-title":"Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading","volume":"1","author":"Wulczyn","year":"2021","journal-title":"Commun Med (Lond)"},{"issue":"1","key":"2025041607173293600_ref10","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","article-title":"Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer","volume":"2","author":"Nagpal","year":"2019","journal-title":"npj Digital Medicine"},{"issue":"1","key":"2025041607173293600_ref11","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1038\/s41585-023-00796-1","article-title":"Artificial intelligence and urology: ethical considerations for urologists and patients","volume":"21","author":"Cacciamani","year":"2024","journal-title":"Nat Rev Urol"},{"issue":"4","key":"2025041607173293600_ref12","doi-asserted-by":"publisher","first-page":"567","DOI":"10.1111\/bju.14852","article-title":"Current status of artificial intelligence applications in urology and their potential to influence clinical practice","volume":"124","author":"Chen","year":"2019","journal-title":"BJU Int"},{"key":"2025041607173293600_ref13","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1017\/9781108669016.014","volume-title":"SHIHbot: Sexual Health Information on HIV\/AIDS, Chatbot, in Artificial Intelligence and Social Work","author":"Tambe","year":"2018"},{"issue":"1","key":"2025041607173293600_ref14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/1364557032000119616","article-title":"Scoping studies: towards a methodological framework","volume":"8","author":"Arksey","year":"2005","journal-title":"Int J Soc Res Methodol"},{"issue":"7","key":"2025041607173293600_ref15","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann Intern Med"},{"issue":"5","key":"2025041607173293600_ref16","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1071\/SH21123","article-title":"Barriers and facilitators to engagement with artificial intelligence (AI)-based chatbots for sexual and reproductive health advice: a qualitative analysis","volume":"18","author":"Nadarzynski","year":"2021","journal-title":"Sex Health"},{"issue":"5","key":"2025041607173293600_ref17","doi-asserted-by":"publisher","first-page":"514","DOI":"10.1016\/j.jadohealth.2010.09.002","article-title":"An artificially intelligent chat agent that answers adolescents' questions related to sex, drugs, and alcohol: an exploratory study","volume":"48","author":"Crutzen","year":"2011","journal-title":"J Adolesc Health"},{"issue":"2","key":"2025041607173293600_ref18","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1080\/00926239408403420","article-title":"Learning about sex outside the gutter: attitudes toward a computer sex-expert system","volume":"20","author":"Ochs","year":"1994","journal-title":"J Sex Marital Ther"},{"issue":"1","key":"2025041607173293600_ref19","doi-asserted-by":"publisher","first-page":"e29969","DOI":"10.2196\/29969","article-title":"An artificial intelligence Chatbot for young People's sexual and reproductive health in India (SnehAI): instrumental case study","volume":"24","author":"Wang","year":"2022","journal-title":"J Med Internet Res"},{"key":"2025041607173293600_ref20","article-title":"TeleNanu, a chatbot for tele-guidance to adolscents and young people on sexual and reproductive health","volume":"33","author":"Baca","year":"2022","journal-title":"Revista Cubana de Informocion en Ciencias de la Salud"},{"key":"2025041607173293600_ref21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3411764.3445694","article-title":"AdolescentBot: understanding opportunities for Chatbots in combating adolescent sexual and reproductive health problems in Bangladesh","volume-title":"Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21)","author":"Rahman","year":"2021"},{"issue":"11","key":"2025041607173293600_ref22","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1177\/09564624231180777","article-title":"\"But can chatbots understand sex?\" Attitudes towards artificial intelligence chatbots amongst sexual and reproductive health professionals: an exploratory mixed-methods study","volume":"34","author":"Nadarzynski","year":"2023","journal-title":"Int J STD AIDS"},{"issue":"3","key":"2025041607173293600_ref23","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1136\/bmjsrh-2018-200271","article-title":"Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice","volume":"46","author":"Nadarzynski","year":"2020","journal-title":"BMJ Sex Reprod Health"},{"key":"2025041607173293600_ref24","doi-asserted-by":"publisher","DOI":"10.3389\/fcomm.2023.1275127","article-title":"Chatbots for embarrassing and stigmatizing conditions: could chatbots encourage users to seek medical advice? Frontiers","volume":"8","author":"Branley-Bell","year":"2023","journal-title":"Communication"},{"issue":"4","key":"2025041607173293600_ref25","doi-asserted-by":"publisher","first-page":"2269008","DOI":"10.1080\/26410397.2023.2269008","article-title":"Assessing acceptability and effectiveness of a pleasure-oriented sexual and reproductive health chatbot in Kenya: an exploratory mixed-methods study","volume":"31","author":"Njogu","year":"2023","journal-title":"Sex Reprod Health Matters"},{"key":"2025041607173293600_ref26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00224499.2023.2254772","article-title":"Hey ChatGPT, Let's talk about sexual consent","author":"Marcantonio","year":"2023","journal-title":"J Sex Res"},{"issue":"4","key":"2025041607173293600_ref27","doi-asserted-by":"publisher","first-page":"e791","DOI":"10.1093\/pubmed\/fdad078","article-title":"ChatGPT educates college students about sexual violence and its impact on their mental health: a proposal","volume":"45","author":"Folastri","year":"2023","journal-title":"J Public Health (Oxf)"},{"issue":"1","key":"2025041607173293600_ref28","doi-asserted-by":"publisher","first-page":"e03218","DOI":"10.1016\/j.heliyon.2020.e03218","article-title":"Applying artificial intelligence to explore sexual cyberbullying behaviour","volume":"6","author":"S\u00e1nchez-Medina","year":"2020","journal-title":"Heliyon"},{"issue":"10","key":"2025041607173293600_ref29","doi-asserted-by":"publisher","first-page":"e42055","DOI":"10.2196\/42055","article-title":"Formative evaluation of the acceptance of HIV prevention artificial intelligence Chatbots by men who have sex with men in Malaysia: focus group study","volume":"6","author":"Peng","year":"2022","journal-title":"JMIR Form Res"},{"key":"2025041607173293600_ref30","doi-asserted-by":"publisher","first-page":"e42983","DOI":"10.2196\/42983","article-title":"Increasing participation in a TelePrEP program for sexual and gender minority adolescents and young adults in Louisiana: protocol for an SMS text messaging-based Chatbot","volume":"12","author":"Braddock","year":"2023","journal-title":"JMIR Res Protoc"},{"issue":"6","key":"2025041607173293600_ref31","doi-asserted-by":"publisher","first-page":"793","DOI":"10.4103\/idoj.idoj_426_23","article-title":"The role and impact of artificial intelligence in addressing sexually transmitted infections, nonvenereal genital diseases, sexual health, and wellness","volume":"14","author":"Mehta","year":"2023","journal-title":"Indian Dermatol Online J"},{"key":"2025041607173293600_ref32","doi-asserted-by":"publisher","first-page":"e43841","DOI":"10.2196\/43841","article-title":"Misinformation and public health messaging in the early stages of the Mpox outbreak: mapping the twitter narrative with deep learning","volume":"25","author":"Edinger","year":"2023","journal-title":"J Med Internet Res"},{"issue":"5","key":"2025041607173293600_ref33","doi-asserted-by":"publisher","first-page":"870","DOI":"10.1007\/s10439-023-03196-z","article-title":"Talk with ChatGPT about the outbreak of Mpox in 2022: reflections and suggestions from AI dimensions","volume":"51","author":"Cheng","year":"2023","journal-title":"Ann Biomed Eng"},{"key":"2025041607173293600_ref34","doi-asserted-by":"publisher","first-page":"e41881","DOI":"10.2196\/41881","article-title":"A transgender Chatbot (Amanda selfie) to create pre-exposure prophylaxis demand among adolescents in Brazil: assessment of acceptability, functionality, usability, and results","volume":"25","author":"Massa","year":"2023","journal-title":"J Med Internet Res"},{"key":"2025041607173293600_ref35","first-page":"422","article-title":"Towards the inference of social and Behavioral determinants of sexual health: development of a gold-standard corpus with semi-supervised learning","volume":"2018","author":"Feller","year":"2018","journal-title":"AMIA Annu Symp Proc"},{"issue":"3","key":"2025041607173293600_ref36","doi-asserted-by":"publisher","first-page":"170","DOI":"10.24198\/jkp.v10i3.2104","article-title":"Artificial intelligence as an educational media to improve adolescent reproductive health: research and development studies","volume":"10","author":"Handayani","year":"2022","journal-title":"Jurnal Keperawatan Padjadjaran"},{"issue":"10279","key":"2025041607173293600_ref37","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/S0140-6736(21)00395-0","article-title":"Epidemiology of HIV in the USA: epidemic burden, inequities, contexts, and responses","volume":"397","author":"Sullivan","year":"2021","journal-title":"Lancet"},{"issue":"4","key":"2025041607173293600_ref38","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1007\/s10461-020-03054-2","article-title":"DOT diary: developing a novel mobile app using artificial intelligence and an electronic sexual diary to measure and support PrEP adherence among young men who have sex with men","volume":"25","author":"Liu","year":"2021","journal-title":"AIDS Behav"},{"issue":"S1","key":"2025041607173293600_ref39","doi-asserted-by":"publisher","first-page":"S20","DOI":"10.1097\/QAI.0000000000002807","article-title":"A peer-led, artificial intelligence-augmented social network intervention to prevent HIV among youth experiencing homelessness","volume":"88","author":"Rice","year":"2021","journal-title":"J Acquir Immune Defic Syndr"},{"issue":"8","key":"2025041607173293600_ref40","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s11121-021-01255-2","article-title":"Using machine learning to predict young People's internet health and social service information seeking","volume":"22","author":"Comulada","year":"2021","journal-title":"Prev Sci"},{"issue":"9","key":"2025041607173293600_ref41","doi-asserted-by":"publisher","first-page":"e1009336","DOI":"10.1371\/journal.pcbi.1009336","article-title":"Incorporating metadata in HIV transmission network reconstruction: a machine learning feasibility assessment","volume":"17","author":"Mazrouee","year":"2021","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"2025041607173293600_ref42","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1007\/s11121-023-01561-x","article-title":"Comparing factors associated with increased stimulant use in relation to HIV status using a machine learning and prediction Modeling approach","volume":"24","author":"Blair","year":"2023","journal-title":"Prev Sci"},{"issue":"7","key":"2025041607173293600_ref43","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11071818","article-title":"A machine-learning-based risk-prediction tool for HIV and sexually transmitted infections acquisition over the next 12 months","volume":"11","author":"Xu","year":"2022","journal-title":"J Clin Med"},{"issue":"6","key":"2025041607173293600_ref44","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1007\/s11121-019-01019-z","article-title":"Using smartphone survey data and machine learning to identify situational and contextual risk factors for HIV risk behavior among men who have sex with men who are not on PrEP","volume":"20","author":"Wray","year":"2019","journal-title":"Prev Sci"},{"issue":"1","key":"2025041607173293600_ref45","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.jinf.2020.11.007","article-title":"Predicting the diagnosis of HIV and sexually transmitted infections among men who have sex with men using machine learning approaches","volume":"82","author":"Bao","year":"2021","journal-title":"J Inf Secur"},{"issue":"10","key":"2025041607173293600_ref46","doi-asserted-by":"publisher","first-page":"e696","DOI":"10.1016\/S2352-3018(19)30139-0","article-title":"Development and validation of an automated HIV prediction algorithm to identify candidates for pre-exposure prophylaxis: a modelling study","volume":"6","author":"Krakower","year":"2019","journal-title":"Lancet HIV"},{"issue":"2","key":"2025041607173293600_ref47","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1007\/s10461-016-1628-y","article-title":"Factors associated with HIV testing among participants from substance use disorder treatment programs in the US: a machine learning approach","volume":"21","author":"Pan","year":"2017","journal-title":"AIDS Behav"},{"issue":"12","key":"2025041607173293600_ref48","doi-asserted-by":"publisher","DOI":"10.2196\/30304","article-title":"Utility of a machine-guided tool for assessing risk behavior associated with contracting HIV in three sites in South Africa: protocol for an In-field evaluation","volume":"10","author":"Majam","year":"2021","journal-title":"JMIR Res Protoc"},{"key":"2025041607173293600_ref49","doi-asserted-by":"publisher","first-page":"101192","DOI":"10.1016\/j.imu.2023.101192","article-title":"Utility of a machine-guided tool for assessing risk behaviour associated with contracting HIV in three sites in South Africa","volume":"37","author":"Majam","year":"2023","journal-title":"Inform Med Unlocked"},{"issue":"1","key":"2025041607173293600_ref50","doi-asserted-by":"publisher","first-page":"8757","DOI":"10.1038\/s41598-022-12033-7","article-title":"Using machine learning approaches to predict timely clinic attendance and the uptake of HIV\/STI testing post clinic reminder messages","volume":"12","author":"Xu","year":"2022","journal-title":"Sci Rep"},{"issue":"4","key":"2025041607173293600_ref51","doi-asserted-by":"publisher","first-page":"e32405","DOI":"10.2196\/32405","article-title":"Toward using twitter for PrEP-related interventions: an automated natural language processing pipeline for identifying gay or bisexual men in the United States","volume":"8","author":"Klein","year":"2022","journal-title":"JMIR Public Health Surveill"},{"issue":"11","key":"2025041607173293600_ref52","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1093\/jamia\/ocz070","article-title":"Network context matters: graph convolutional network model over social networks improves the detection of unknown HIV infections among young men who have sex with men","volume":"26","author":"Xiang","year":"2019","journal-title":"J Am Med Inform Assoc"},{"issue":"4","key":"2025041607173293600_ref53","doi-asserted-by":"publisher","first-page":"e22042","DOI":"10.2196\/22042","article-title":"Leveraging social media activity and machine learning for HIV and substance abuse risk assessment: development and validation study","volume":"23","author":"Ovalle","year":"2021","journal-title":"J Med Internet Res"},{"issue":"1","key":"2025041607173293600_ref54","doi-asserted-by":"publisher","first-page":"e54","DOI":"10.1016\/S2352-3018(21)00247-2","article-title":"Application of artificial intelligence and machine learning for HIV prevention interventions","volume":"9","author":"Xiang","year":"2022","journal-title":"Lancet HIV"},{"issue":"2","key":"2025041607173293600_ref55","doi-asserted-by":"publisher","first-page":"237","DOI":"10.5534\/wjmh.220213","article-title":"The renaissance of male infertility Management in the Golden age of andrology","volume":"41","author":"Calogero","year":"2023","journal-title":"World J Mens Health"},{"issue":"3","key":"2025041607173293600_ref56","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1093\/biolre\/ioac105","article-title":"Advanced molecular approaches in male infertility diagnosis\u2020","volume":"107","author":"Botezatu","year":"2022","journal-title":"Biol Reprod"},{"issue":"3","key":"2025041607173293600_ref57","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1111\/andr.13141","article-title":"Machine learning based prediction models in male reproductive health: development of a proof-of-concept model for Klinefelter syndrome in azoospermic patients","volume":"10","author":"Krenz","year":"2022","journal-title":"Andrology"},{"issue":"1","key":"2025041607173293600_ref58","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1097\/01.ju.0000146839.43427.0b","article-title":"Computational models for detection of erectile dysfunction","volume":"173","author":"Wald","year":"2005","journal-title":"J Urol"},{"key":"2025041607173293600_ref59","doi-asserted-by":"publisher","first-page":"105244","DOI":"10.1016\/j.phrs.2020.105244","article-title":"Artificial intelligence based identification of the functional role of hirudin in diabetic erectile dysfunction treatment","volume":"163","author":"Yang","year":"2021","journal-title":"Pharmacol Res"},{"issue":"5","key":"2025041607173293600_ref60","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1109\/JBHI.2018.2877595","article-title":"Design of a Clinical Decision Support System for predicting erectile dysfunction in men using NHIRD dataset","volume":"23","author":"Chen","year":"2019","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025041607173293600_ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.fsigen.2021.102602","article-title":"Sperm hunting on optical microscope slides for forensic analysis with deep convolutional networks - a feasibility study","volume":"56","author":"Golomingi","year":"2022","journal-title":"Forensic Sci Int Genet"},{"issue":"10","key":"2025041607173293600_ref62","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1097\/GME.0000000000002246","article-title":"Appropriateness of premature ovarian insufficiency recommendations provided by ChatGPT","volume":"30","author":"S\u00fctc\u00fco\u011flu","year":"2023","journal-title":"Menopause"},{"key":"2025041607173293600_ref63","doi-asserted-by":"publisher","DOI":"10.1109\/DISCOVER55800.2022.9974709","article-title":"Deep learning approaches to determine gender based on digital bones of skeleton: a survey","volume-title":"2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics ( DISCOVER)","author":"Janardhan","year":"2022"},{"issue":"6","key":"2025041607173293600_ref64","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1016\/j.scijus.2021.10.002","article-title":"Prognosis of sexual dimorphism with unfused hyoid bone: artificial intelligence informed decision making with discriminant analysis","volume":"61","author":"Tyagi","year":"2021","journal-title":"Sci Justice"},{"issue":"7","key":"2025041607173293600_ref65","doi-asserted-by":"publisher","first-page":"4013","DOI":"10.1093\/cercor\/bhac323","article-title":"Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity","volume":"33","author":"Clemens","year":"2023","journal-title":"Cereb Cortex"},{"issue":"1","key":"2025041607173293600_ref66","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1177\/00258024221089073","article-title":"Deep learning and morphometric approach for sex determination of the lumbar vertebrae in a Thai population","volume":"63","author":"Malatong","year":"2023","journal-title":"Med Sci Law"},{"key":"2025041607173293600_ref67","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jflm.2017.08.011","article-title":"Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers","volume":"52","author":"Curate","year":"2017","journal-title":"J Forensic Leg Med"},{"issue":"1","key":"2025041607173293600_ref68","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1186\/s12903-023-03033-8","article-title":"Detecting representative characteristics of different genders using intraoral photographs: a deep learning model with interpretation of gradient-weighted class activation mapping","volume":"23","author":"Zhou","year":"2023","journal-title":"BMC Oral Health"},{"key":"2025041607173293600_ref69","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.18280\/ts.390515","article-title":"Human gender prediction based on deep transfer learning from panoramic radiograph images","author":"Ata\u015f","year":"2022"},{"issue":"6","key":"2025041607173293600_ref70","doi-asserted-by":"publisher","first-page":"97","DOI":"10.3390\/data8060097","article-title":"A fast deep learning ECG sex identifier based on wavelet RGB image classification","volume":"8","author":"Cabra Lopez","year":"2023","journal-title":"Data"},{"key":"2025041607173293600_ref71","doi-asserted-by":"publisher","first-page":"1001085","DOI":"10.3389\/fpsyt.2023.1001085","article-title":"Patterns of risk-using machine learning and structural neuroimaging to identify pedophilic offenders","volume":"14","author":"Popovic","year":"2023","journal-title":"Front Psychiatry"},{"issue":"18","key":"2025041607173293600_ref72","doi-asserted-by":"publisher","first-page":"9285","DOI":"10.3390\/app12189285","article-title":"A generalised neural network model to estimate sex from cranial metric traits: a robust training and testing approach","volume":"12","author":"Del Bove","year":"2022","journal-title":"Appl Sci"},{"issue":"1","key":"2025041607173293600_ref73","doi-asserted-by":"publisher","first-page":"9599","DOI":"10.1038\/s41598-022-13642-y","article-title":"Individualized identification of sexual dysfunction of psychiatric patients with machine-learning","volume":"12","author":"Liu","year":"2022","journal-title":"Sci Rep"},{"issue":"2","key":"2025041607173293600_ref74","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1080\/00224499.2021.1967846","article-title":"Is infidelity predictable? Using explainable machine learning to identify the most important predictors of infidelity","volume":"59","author":"Vowels","year":"2022","journal-title":"J Sex Res"},{"issue":"7","key":"2025041607173293600_ref75","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1016\/j.jsxm.2021.04.010","article-title":"Uncovering the most important factors for predicting sexual desire using explainable machine learning","volume":"18","author":"Vowels","year":"2021","journal-title":"J Sex Med"},{"issue":"4","key":"2025041607173293600_ref76","doi-asserted-by":"publisher","first-page":"476","DOI":"10.34107\/KSZV7781.10476","article-title":"Mengo: a novel cloud-based digital healthcare platform for andrology powered by artificial intelligence, Data Science & Analytics, bio- informatics and Blockchain","volume":"57","author":"Ray","year":"2021","journal-title":"Biomed Sci Instrum"},{"issue":"9","key":"2025041607173293600_ref77","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.33164","article-title":"Ecological momentary assessment and machine learning for predicting suicidal ideation among sexual and gender minority individuals","volume":"6","author":"Lei","year":"2023","journal-title":"JAMA Netw Open"},{"issue":"2","key":"2025041607173293600_ref78","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1097\/ADM.0000000000001078","article-title":"Logistic regression with machine learning sheds light on the problematic sexual behavior phenotype","volume":"17","author":"Jiang","year":"2023","journal-title":"J Addict Med"},{"issue":"32","key":"2025041607173293600_ref79","doi-asserted-by":"publisher","first-page":"19061","DOI":"10.1073\/pnas.1917036117","article-title":"Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies","volume":"117","author":"Joel","year":"2020","journal-title":"Proc Natl Acad Sci USA"},{"key":"2025041607173293600_ref80","doi-asserted-by":"publisher","DOI":"10.1145\/3586182.3616689","article-title":"Towards trauma-informed data donation of sexual experience in online dating to improve sexual risk detection AI","volume-title":"Adjunct Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology (UIST '23 Adjunct)","author":"Zheng","year":"2023"},{"issue":"5","key":"2025041607173293600_ref81","doi-asserted-by":"publisher","first-page":"781","DOI":"10.5811\/westjem.2022.7.56726","article-title":"A novel technique to identify intimate partner violence in a hospital setting","volume":"23","author":"Tabaie","year":"2022","journal-title":"West J Emerg Med"},{"issue":"1920","key":"2025041607173293600_ref82","doi-asserted-by":"publisher","DOI":"10.4414\/SMW.2022.w30179","article-title":"Are we ready for artificial intelligence in medicine?","volume":"152","author":"Iqbal","year":"2022","journal-title":"Swiss Med Wkly"},{"key":"2025041607173293600_ref83","doi-asserted-by":"publisher","volume-title":"Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue. Saarbr\u00fccken, Germany. Association for Computational Linguistics.","author":"Brixey","DOI":"10.18653\/v1\/W17-5544"},{"key":"2025041607173293600_ref84","doi-asserted-by":"publisher","DOI":"10.2196\/46761","article-title":"Chatbots to improve sexual and reproductive health: realist synthesis","volume":"25","author":"Mills","year":"2023","journal-title":"J Med Internet Res"},{"key":"2025041607173293600_ref85","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/B978-0-12-818438-7.00002-2","volume-title":"Artificial Intelligence in Healthcare","author":"Bohr","year":"2020"},{"key":"2025041607173293600_ref86","doi-asserted-by":"publisher","first-page":"1226776","DOI":"10.3389\/fpubh.2023.1226776","article-title":"AI chatbots and (mis)information in public health: impact on vulnerable communities","volume":"11","author":"Meyrowitsch","year":"2023","journal-title":"Front Public Health"},{"issue":"10","key":"2025041607173293600_ref87","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.jcjq.2020.08.002","article-title":"Considering the safety and quality of artificial intelligence in health care","volume":"46","author":"Ross","year":"2020","journal-title":"Jt Comm J Qual Patient Saf"},{"issue":"1","key":"2025041607173293600_ref88","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s12910-021-00687-3","article-title":"Privacy and artificial intelligence: challenges for protecting health information in a new era","volume":"22","author":"Murdoch","year":"2021","journal-title":"BMC Med Ethics"},{"key":"2025041607173293600_ref89","doi-asserted-by":"publisher","first-page":"927721","DOI":"10.3389\/fgene.2022.927721","article-title":"The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union","volume":"13","author":"Meszaros","year":"2022","journal-title":"Front Genet"},{"issue":"1150","key":"2025041607173293600_ref90","doi-asserted-by":"publisher","first-page":"20230023","DOI":"10.1259\/bjr.20230023","article-title":"AI pitfalls and what not to do: mitigating bias in AI","volume":"96","author":"Gichoya","year":"2023","journal-title":"Br J Radiol"},{"key":"2025041607173293600_ref91","doi-asserted-by":"publisher","first-page":"103358","DOI":"10.1016\/j.ebiom.2021.103358","article-title":"Ensuring that biomedical AI benefits diverse populations","volume":"67","author":"Zou","year":"2021","journal-title":"EBioMedicine"},{"key":"2025041607173293600_ref92","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.breast.2019.10.001","article-title":"The ethical, legal and social implications of using artificial intelligence systems in breast cancer care","volume":"49","author":"Carter","year":"2020","journal-title":"Breast"},{"issue":"3","key":"2025041607173293600_ref93","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1016\/j.scijus.2022.03.003","article-title":"Performance of the supervised learning algorithms in sex estimation of the proximal femur: a comparative study in contemporary Egyptian and Turkish samples","volume":"62","author":"Attia","year":"2022","journal-title":"Sci Justice"}],"container-title":["Sexual Medicine Reviews"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/smr\/article-pdf\/13\/2\/267\/62524527\/qeaf009.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/smr\/article-pdf\/13\/2\/267\/62524527\/qeaf009.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T11:18:09Z","timestamp":1744802289000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/smr\/article\/13\/2\/267\/8090551"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,23]]},"references-count":93,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,3,23]]},"published-print":{"date-parts":[[2025,4,14]]}},"URL":"https:\/\/doi.org\/10.1093\/sxmrev\/qeaf009","relation":{},"ISSN":["2050-0513","2050-0521"],"issn-type":[{"value":"2050-0513","type":"print"},{"value":"2050-0521","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,4]]},"published":{"date-parts":[[2025,3,23]]}}}