{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T13:00:02Z","timestamp":1791464402821,"version":"4.3.4"},"reference-count":38,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Histopathology"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:sec><jats:title>Aims<\/jats:title><jats:p>As pathology departments transition towards digital workflows, the integration of artificial intelligence (AI) is anticipated to become increasingly common. This study aimed to describe the real\u2010world implementation and impact of AI integration in routine pathological diagnostics, specifically focusing on prostate biopsy evaluations at the Department of Pathology, ZAS Hospitals, Antwerp.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods and Results<\/jats:title><jats:p>An AI tool for analysing prostate biopsies was integrated into the department's daily workflow by embedding it into existing laboratory information and reporting systems. Following a short adaptation period, the use of AI led to measurable improvements. Most notably, there was a reduction in the number of immunohistochemical tests required, indicating more confident primary diagnoses. Additionally, a significant decrease in turnaround times for biopsy evaluations was observed, highlighting improved efficiency. The implementation process was closely monitored, and practical insights were gathered to guide future AI deployments in pathology.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The first\u2010year experience of integrating AI into daily pathological practice demonstrated tangible benefits in diagnostic efficiency and workflow optimization. However, the process also revealed several challenges related to real\u2010world deployment, including adaptation by staff and system integration hurdles. The lessons learned provide valuable guidance for other institutions considering similar AI implementations, reinforcing the importance of strategic planning, training and system compatibility in successful adoption.<\/jats:p><\/jats:sec>","DOI":"10.1111\/his.15481","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T04:58:41Z","timestamp":1750136321000},"page":"635-646","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Practical implementation of <scp>AI<\/scp> in a non\u2010academic, non\u2010commercial Pathology laboratory: Real world experience\u00a0and lessons learned"],"prefix":"10.1111","volume":"87","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6925-571X","authenticated-orcid":false,"given":"Frederik","family":"Deman","sequence":"first","affiliation":[{"name":"PA<sup>2<\/sup>, Department of Pathology Ziekenhuis aan de Stroom (ZAS)  Antwerp Belgium"},{"name":"Department 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