ArticleJournal of pathology informatics2026
Validation, implementation, and impact of an AI model in routine practice for pathologic diagnosis of prostate cancer in an academic medical center.
Article in Journal of pathology informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Regulatory science for AI-basedJournal of pathology informatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pathological evaluation of prostate needle biopsies is labor-intensive and often requires ancillary immunohistochemistry (IHC), increasing cost and diagnostic turnaround time (TAT). Artificial intelligence (AI)-based decision-support tools may improve efficiency, but clinical deployment requires institutional validation and assessment of real-world impact. Methods: Within a fully digital pathology practice, we performed institutional validation of an AI-assisted prostate biopsy decision-support tool using routine clinical cases, with pathologist-rendered diagnoses as ground truth. Following validation, the tool was implemented into the routine sign-out. A retrospective pre-post analysis compared prostate biopsy cases signed out during 3-month periods before and after implementation, excluding a transition month. Diagnostic TAT was defined as the interval from whole-slide image scan completion to final sign-out. IHC utilization was recorded. Weighted median TATs and IHC use were compared using standard statistical methods. Results: The validation cohort met all predefined acceptance criteria, demonstrating high AI performance (sensitivity 91-100%, specificity 99%, positive-predictive value 98%, negative-predictive value 96%, area under the curve 0.97). Following clinical implementation, diagnostic TAT decreased by 30% and IHC utilization decreased by 38%. Conclusions: Institutional validation and clinical implementation of an AI-assisted prostate biopsy decision-support tool were associated with significant reductions in diagnostic TAT and IHC utilization. When deployed as an adjunct within a digital workflow, AI assistance may enhance efficiency while preserving pathologist responsibility for final diagnosis.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.