# Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists

arXiv: https://arxiv.org/abs/2002.04500 · HTML: https://mlsanity.com/paper/2002.04500/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2020.

## Built on by (most cited first, 9 of 9)

- [Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology](https://mlsanity.com/paper/2206.01794/) (2022)
- [A Survey on Graph-Based Deep Learning for Computational Histopathology](https://mlsanity.com/paper/2107.00272/) (2021)
- [Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging](https://mlsanity.com/paper/2111.01556/) (2021)
- [Unsupervised anomaly detection in digital pathology using GANs](https://mlsanity.com/paper/2103.08945/) (2021)
- [SC-MIL: Supervised Contrastive Multiple Instance Learning for Imbalanced Classification in Pathology](https://mlsanity.com/paper/2303.13405/) (2023)
- [Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition](https://mlsanity.com/paper/2501.10909/) (2025)
- [Bridging the gap between prostate radiology and pathology through machine learning](https://mlsanity.com/paper/2112.02164/) (2021)
- [Critical Evaluation of Artificial Intelligence as Digital Twin of Pathologist for Prostate Cancer Pathology](https://mlsanity.com/paper/2308.11992/) (2023)
- [AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples](https://mlsanity.com/paper/2601.21022/) (2026)

## 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.