{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T10:12:20Z","timestamp":1775038340250,"version":"3.50.1"},"reference-count":14,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T00:00:00Z","timestamp":1739404800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The Prostate"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>To evaluate the progression\u2010free survival (PFS) time in patients with early\u2010stage and locally advanced prostate cancer and to compare the estimates provided by ChatGPT with actual survival data.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A retrospective analysis was conducted on patients diagnosed with early\u2010stage\/locally advanced prostate cancer. Each patient's estimated PFS times were calculated using an artificial intelligence chatbot. These estimates were generated by considering several factors, including the patient's clinical characteristics, tumor stage, treatment modalities, and biochemical parameters. A statistical comparison was conducted between the predicted PFS and actual PFS times.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The AI chatbot tended to overestimate the overall PFS times. A statistically significant discrepancy was observed between the predicted and actual survival times (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). A discrepancy of 9.19 months was observed between the PFS predictions made by ChatGPT and the actual PFS. The bias value was 48.57, yet this discrepancy had a negligible impact on clinical practice (Cohen's <jats:italic>d<\/jats:italic>\u2009=\u20090.189).<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Artificial intelligence\u2010based models have the potential to play an important role in the prediction of progression in cancers such as prostate cancer, where 5\u201310\u2010year survival rates can reach 100%. However, this study's findings indicate that the AI model's predictions are not aligned with the actual clinical data. The reliability of the integration of artificial intelligence into clinical decision support systems can be enhanced through the undertaking of comprehensive future studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The use of artificial intelligence in predictive modeling may prove an effective approach for forecasting the PFS of prostate cancer. It has the potential to supplant the nomograms that are currently in use. Nevertheless, further studies are required to substantiate the accuracy and reliability of these systems.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pros.24871","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T05:49:42Z","timestamp":1739512182000},"page":"677-683","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Progression\u2010Free Survival Prediction Performance of ChatGPT: Analysis With Real Life Data in Early and Locally Advanced Prostate Cancer"],"prefix":"10.1002","volume":"85","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3247-5361","authenticated-orcid":false,"given":"Engin Eren","family":"Kavak","sequence":"first","affiliation":[{"name":"Department of Medical Oncology Etlik City Hospital, Oncology Hospital Ankara Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0130smail","family":"Dilli","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology Etlik City Hospital, Oncology Hospital Ankara Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Surveillance Research Program N. C. I. \u201cSEER*Explorer: An Interactive Website for SEER Cancer Statistics.\u201d2024 https:\/\/seer.cancer.gov\/statistics-network\/explorer\/."},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.18.00606"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/JU.0000000000002757"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.ju.0000127733.01845.57"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.26169"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-016-0599-5"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMc2406108"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2013.09.002"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2008.20.8983"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.juro.2011.12.015"},{"issue":"10","key":"e_1_2_11_12_1","first-page":"820","article-title":"Machine Learning Applications in Breast Cancer Survival and Therapeutic Outcome Prediction Based on Multi\u2010Omic Analysis","volume":"46","author":"Zhang Z. Y.","year":"2024","journal-title":"Yi chuan = Hereditas"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2024.1342937"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014-024-02519-1"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.2196\/56532"}],"container-title":["The Prostate"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/pros.24871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T06:03:16Z","timestamp":1744783396000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/pros.24871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,13]]},"references-count":14,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["10.1002\/pros.24871"],"URL":"https:\/\/doi.org\/10.1002\/pros.24871","archive":["Portico"],"relation":{},"ISSN":["0270-4137","1097-0045"],"issn-type":[{"value":"0270-4137","type":"print"},{"value":"1097-0045","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,13]]},"assertion":[{"value":"2024-11-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-06","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-02-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}