{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T15:54:28Z","timestamp":1787932468626,"version":"build-2784847793"},"reference-count":9,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Andrology"],"abstract":"ABSTRACT<\/jats:title>\n \n \n Artificial intelligence (AI) is increasingly entering male infertility care through semen image analysis, sperm\u2010selection support, prediction of sperm retrieval, and decision\u2010support tools for assisted reproduction. Yet clinical value will depend less on algorithmic novelty than on explainability, governance, and safe integration into existing andrology practice. This Perspective proposes a pragmatic roadmap for explainable AI in male infertility, moving from laboratory quality support to externally validated prediction tools and shared decision\u2010making. Explainability methods such as feature importance, SHAP, LIME, and saliency maps may help clinicians understand which semen, hormonal, imaging, or clinical variables influence an AI output. However, these methods must be combined with privacy protection, bias assessment, laboratory\u2010specific validation, and transparent reporting. AI should support, not replace, standardized semen analysis, urological evaluation, and patient\u2010centered counseling. A responsible implementation pathway should align with medical\u2010device regulation, reporting standards for AI studies, and local audits of performance across diverse populations.<\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/andr.70334","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:54:11Z","timestamp":1784955251000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Explainable AI in Male Infertility: A Clinical Roadmap for Shared Decision\u2010Making"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8852-2875","authenticated-orcid":false,"given":"Stjepan","family":"\u0160kudar","sequence":"first","affiliation":[{"name":"Zagreb West Health Center Zagreb Croatia"},{"name":"DOBA University of Applied Sciences Maribor Slovenia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11934\u2010024\u201001239\u2010z"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40001\u2010025\u201002479\u20106"},{"key":"e_1_2_9_4_1","volume-title":"WHO Laboratory Manual for the Examination and Processing of Human Semen","author":"World Health Organization","year":"2021"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2025.02.026"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010020\u20101034\u2010x"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010020\u20101037\u20107"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj\u20102023\u2010078378"},{"key":"e_1_2_9_9_1","unstructured":"U.S. Food and Drug Administration Health Canada andMedicines and Healthcare Products Regulatory Agency \u201cGood Machine Learning Practice for Medical Device Development: Guiding Principles \u201dU.S. Food and Drug Administration accessed July 11 2026 https:\/\/www.fda.gov\/medical\u2010devices\/software\u2010medical\u2010device\u2010samd\/good\u2010machine\u2010learning\u2010practice\u2010medical\u2010device\u2010development\u2010guiding\u2010principles."},{"key":"e_1_2_9_10_1","unstructured":"European Commission \u201cArtificial Intelligence in Healthcare \u201dEuropean Commission accessed July 11 2026 https:\/\/health.ec.europa.eu\/ehealth\u2010digital\u2010health\u2010and\u2010care\/artificial\u2010intelligence\u2010healthcare_en."}],"container-title":["Andrology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/andr.70334","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/andr.70334","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/andr.70334","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T04:54:12Z","timestamp":1784955252000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/andr.70334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":9,"alternative-id":["10.1111\/andr.70334"],"URL":"https:\/\/doi.org\/10.1111\/andr.70334","archive":["Portico"],"relation":{},"ISSN":["2047-2919","2047-2927"],"issn-type":[{"value":"2047-2919","type":"print"},{"value":"2047-2927","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]},"assertion":[{"value":"2026-05-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-07-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"andr.70334"}}