[1] "2026-05-04 21:56:13 +03"Appendix A — Summary
B Executive Summary
This project evaluates the impact of the Paige Prostate AI tool on the workflow and diagnostic accuracy of pathologists at Memorial Hospitals Group.
Key Findings:
- Agreement: AI assistance significantly improved inter-rater agreement among pathologists (Kappa increased from 0.68 to 0.88).
- Efficiency: Diagnosis duration was reduced by approximately X% (see Duration Analysis).
- Accuracy: AI demonstrated high sensitivity (97.6%) when compared to the gold standard (IHC-confirmed diagnosis).
- Discordance: Most discordant cases involved [specific patterns if known, e.g., small foci of cancer].
- Learning Curve: Pathologists showed a [positive/negative] trend in diagnosis speed over time, suggesting [learning/fatigue].
This report details the methodology, statistical analysis, and clinical implications of these findings.
B.1 Acknowledgement
B.2 System Information
R version 4.5.1 (2025-06-13)
Platform: aarch64-apple-darwin20
Running under: macOS Tahoe 26.4.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
locale:
[1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
time zone: Europe/Istanbul
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] magrittr_2.0.5
loaded via a namespace (and not attached):
[1] htmlwidgets_1.6.4 compiler_4.5.1 fastmap_1.2.0 cli_3.6.6
[5] tools_4.5.1 htmltools_0.5.9 otel_0.2.0 yaml_2.3.12
[9] rmarkdown_2.31 knitr_1.51 jsonlite_2.0.0 xfun_0.57
[13] digest_0.6.39 rlang_1.2.0 evaluate_1.0.5
Part of this study is presented as a poster in European Congress of Digital Pathology (ECDP 2023)