{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T00:03:32Z","timestamp":1792109012269,"version":"4.4.0"},"reference-count":33,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["bjui-journals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BJU International"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To evaluate the effectiveness of a rules\u2010based artificial intelligence (AI) clinical decision support system (CDSS) called the PROState AI Cancer\u2010Decision Support (PROSAIC\u2010DS) in streamlining the prostate cancer multidisciplinary team (MDT) pathway by identifying patients meeting standard of care (SoC) guidelines for reduced discussion in MDT meetings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Subjects\/Patients and Methods<\/jats:title>\n                    <jats:p>This study consisted of two phases. Phase one involved a retrospective concordance analysis of 287 patients referred to the prostate MDT at King's College Hospital over a 2\u2010year period. In phase two, a prospective analysis included 416 patients from Guy's Hospital over another 2\u2010year period. Clinical treatment recommendations were independently reviewed by a panel of urologists and oncologists to establish a \u2018ground truth.\u2019 Concordance between the medical recommendations and those generated by the PROSAIC\u2010DS was assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In phase one, the overall concordance between the clinicians\u2019 recommendations and the PROSAIC\u2010DS was 92% (95% confidence interval [CI] 88.1\u201394.7%), compared to just 53% (95% CI 47\u201359%) with historic MDT outputs (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01). In phase two, the PROSAIC\u2010DS achieved an 85.6% concordance (95% CI 81.6\u201388.9%) with the MDT recommendations for 355 evaluable cases (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01). Notably, using a machine learning\u2010derived decision tree enabled the identification of 93 patients for streamlined management, demonstrating a 97.8% concordance in this subgroup (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u20090.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The implementation of the PROSAIC\u2010DS into the prostate cancer MDT pathway allowed 33.8% of patients to bypass MDT discussions with high treatment concordance. This study showcases the potential for AI\u2010based solutions to improve clinical workflow and patient management in oncology, thus addressing the workload challenges faced by MDTs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/bju.16845","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T07:49:00Z","timestamp":1751356140000},"page":"872-881","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial intelligence\u2010driven streamlining of prostate cancer multidisciplinary team recommendations in a tertiary\n                    <scp>NHS<\/scp>\n                    centre in the UK"],"prefix":"10.1111","volume":"136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7590-3648","authenticated-orcid":false,"given":"Ahmed","family":"Khattak","sequence":"first","affiliation":[{"name":"Guy's and St Thomas\u2019 NHS Foundation Trust  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2024-3267","authenticated-orcid":false,"given":"Danny","family":"Ruta","sequence":"additional","affiliation":[{"name":"Guy's and St Thomas\u2019 NHS Foundation Trust  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vivek","family":"Patkar","sequence":"additional","affiliation":[{"name":"Deontics Ltd.  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rick","family":"Popert","sequence":"additional","affiliation":[{"name":"Guy's and St Thomas\u2019 NHS Foundation Trust  London UK"},{"name":"Deontics Ltd.  London UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Makanjuola","sequence":"additional","affiliation":[{"name":"Deontics Ltd.  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