Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
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Abstract (arXiv)
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems, based on deep learning, have proven to achieve pathologist-level performance at Gleason grading. However, the performance of such systems can degrade in the presence of artifacts, foreign tissue, or other anomalies. Pathologists integrating their expertise with feedback from an AI system could result in a synergy that outperforms both the individual pathologist and the system. Despite the hype around AI assistance, existing literature on this topic within the pathology domain is limited. We investigated the value of AI assistance for grading prostate biopsies. A panel of fourteen observers graded 160 biopsies with and without AI assistance. Using AI, the agreement of the panel with an expert reference standard significantly increased (quadratically weighted Cohen's kappa, 0.799 vs 0.872; p=0.018). Our results show the added value of AI systems for Gleason grading, but more importantly, show the benefits of pathologist-AI synergy.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Feb 2020.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 2; 2024: 0; 2025: 1; 2026: 1.
9 papers.
- 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
- 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
- 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
- 2021Unsupervised anomaly detection in digital pathology using GANscited by 7
- 2023SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathologycited by 5
- 2025Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decompositioncited by 3
- 2021Bridging the gap between prostate radiology and pathology through machine learningcited by 2
- 2023Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathologycited by 0
- 2026AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samplescited by 0
- 2026AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples28 Jan 2026
- 2025Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition19 Jan 2025
- 2023Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology23 Aug 2023
- 2023SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology23 Mar 2023
- 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology3 Jun 2022
- 2021Bridging the gap between prostate radiology and pathology through machine learning3 Dec 2021
- 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging1 Nov 2021
- 2021A Survey on Graph-Based Deep Learning for Computational Histopathology1 Jul 2021
- 2021Unsupervised anomaly detection in digital pathology using GANs16 Mar 2021