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Unleashing the potential of AI for pathology: challenges and recommendations

https://doi.org/10.1002/path.6168
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
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Challenges to the Reproducibility of Machine Learning Models in Health Care
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Geographic Distribution of US Cohorts Used to Train Deep Learning Algorithms
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Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting
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Examining Bias in Studies of Statin Treatment and Survival in Patients With Cancer
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Emerging role of deep learning‐based artificial intelligence in tumor pathology
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Racial difference in histologic subtype of renal cell carcinoma
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Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations
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MIRIAM: A machine and deep learning single‐cell segmentation and quantification pipeline for multi‐dimensional tissue images
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Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association
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Weakly supervised learning on unannotated H&E‐stained slides predicts BRAF mutation in thyroid cancer with high accuracy
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Weakly supervised annotation‐free cancer detection and prediction of genotype in routine histopathology
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The state of the art for artificial intelligence in lung digital pathology
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Deep learning in histopathology: A review
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On Transferability of Histological Tissue Labels in Computational Pathology
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Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles
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All You Need is Color: Image Based Spatial Gene Expression Prediction Using Neural Stain Learning
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Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images
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Computational methods for automated mitosis detection in histopathology images: A review
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Universal encoding of pan-cancer histology by deep texture representations
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The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis
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A review: The detection of cancer cells in histopathology based on machine vision
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WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images
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Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning
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Weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images
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Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade
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Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review
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Immortal Time Bias in National Cancer Database Studies
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Computational pathology: A survey review and the way forward
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Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images
inferredcites →https://doi.org/10.1016/j.media.2020.101771openalex-snapshot
NuClick: A deep learning framework for interactive segmentation of microscopic images
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Deep neural network models for computational histopathology: A survey
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SAFRON: Stitching Across the Frontier Network for Generating Colorectal Cancer Histology Images
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Benchmarking weakly-supervised deep learning pipelines for whole slide classification in computational pathology
inferredcites →https://doi.org/10.1016/j.media.2022.102685openalex-snapshot
One model is all you need: Multi-task learning enables simultaneous histology image segmentation and classification
inferredcites →https://doi.org/10.1016/j.media.2023.102791openalex-snapshot
Weakly supervised histopathology image segmentation with self-attention
inferredcites →https://doi.org/10.1016/j.media.2023.102885openalex-snapshot
An aggregation of aggregation methods in computational pathology
inferredcites →https://doi.org/10.1016/j.mlwa.2021.100198openalex-snapshot
Self supervised contrastive learning for digital histopathology
inferredcites →https://doi.org/10.1016/j.neucom.2021.07.005openalex-snapshot
Towards histopathological stain invariance by Unsupervised Domain Augmentation using generative adversarial networks
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Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
inferredcites →https://doi.org/10.1016/j.patter.2023.100804openalex-snapshot
Leakage and the reproducibility crisis in machine-learning-based science
inferredcites →https://doi.org/10.1016/s0140-6736(20)32402-8openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1470-2045(21)00533-7openalex-snapshot
Artificial intelligence and cancer diagnosis: caution needed
inferredcites →https://doi.org/10.1016/s2589-7500(20)30065-0openalex-snapshot
Ethical limitations of algorithmic fairness solutions in health care machine learning
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Health data poverty: an assailable barrier to equitable digital health care
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1,500 scientists lift the lid on reproducibility
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Millions of black people affected by racial bias in health-care algorithms
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Could machine learning fuel a reproducibility crisis in science?
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Artificial intelligence and computational pathology
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Prospective multi-institutional evaluation of pathologist assessment of PD-L1 assays for patient selection in triple negative breast cancer
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Digital pathology and artificial intelligence in translational medicine and clinical practice
inferredcites →https://doi.org/10.1038/s41379-022-01147-yopenalex-snapshot
Recommendations on compiling test datasets for evaluating artificial intelligence solutions in pathology
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Deep learning in cancer pathology: a new generation of clinical biomarkers
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Adoption of artificial intelligence in breast imaging: evaluation, ethical constraints and limitations
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Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning
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Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology
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Data-efficient and weakly supervised computational pathology on whole-slide images
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Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning
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AI-based pathology predicts origins for cancers of unknown primary
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Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
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Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
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Deep learning in histopathology: the path to the clinic
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Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours
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Weakly-supervised learning for lung carcinoma classification using deep learning
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Generalisation effects of predictive uncertainty estimation in deep learning for digital pathology
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Deep learning-based breast cancer grading and survival analysis on whole-slide histopathology images
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Generalization of vision pre-trained models for histopathology
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The need for uncertainty quantification in machine-assisted medical decision making
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Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis
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Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
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A user-friendly tool for cloud-based whole slide image segmentation with examples from renal histopathology
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The FAIR Guiding Principles for scientific data management and stewardship
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2021 Year in Review
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NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer
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Overcoming the challenges to implementation of artificial intelligence in pathology
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Impact of Histology on Racial Disparities in Epithelial Ovarian Cancer Patients [39R]
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ArcticAI: A Deep Learning Platform for Rapid and Accurate Histological Assessment of Intraoperative Tumor Margins
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Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
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Self-Path: Self-Supervision for Classification of Pathology Images With Limited Annotations
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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inferred← citeshttps://doi.org/10.1111/bph.70585openalex-snapshot
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inferred← citeshttps://doi.org/10.1111/his.70020openalex-snapshot
Artificial intelligence in genitourinary pathology
inferred← citeshttps://doi.org/10.1111/jog.70052openalex-snapshot
Nitabuch's and Rohr's fibrinoid layers: Revisiting the interface of the placenta and uterus
inferred← citeshttps://doi.org/10.1136/jitc-2025-014429openalex-snapshot
Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application
inferred← citeshttps://doi.org/10.1145/3724391openalex-snapshot
Domain Generalization in Computational Pathology: Survey and Guidelines
inferred← citeshttps://doi.org/10.1186/s12943-025-02369-9openalex-snapshot
Current AI technologies in cancer diagnostics and treatment
inferred← citeshttps://doi.org/10.1186/s13000-024-01590-2openalex-snapshot
The current troubled state of the global pathology workforce: a concise review
inferred← citeshttps://doi.org/10.1186/s13046-025-03592-4openalex-snapshot
The role of artificial intelligence-based foundation models and “copilots” in cancer pathology: potential and challenges
inferred← citeshttps://doi.org/10.1186/s13073-025-01484-yopenalex-snapshot
Closing the gap in the clinical adoption of computational pathology: a standardized, open-source framework to integrate deep-learning models into the laboratory information system
inferred← citeshttps://doi.org/10.1360/tb-2024-0088openalex-snapshot
Five decades of colorectal cancer pathology: The World and China
inferred← citeshttps://doi.org/10.15584/ejcem.2026.1.10openalex-snapshot
Humanized NSG mice ‒ a modern approach to modelling systemic lupus erythematosus in preclinical research
inferred← citeshttps://doi.org/10.26599/bdma.2025.9020072openalex-snapshot
Domain-Specific Self-Supervised Contrastive Learning with Contrast-Aware Pair Refinement for Pathological Image Analysis
inferred← citeshttps://doi.org/10.3389/fmed.2025.1633416openalex-snapshot
Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine
inferred← citeshttps://doi.org/10.3389/frai.2025.1517670openalex-snapshot
The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review
inferred← citeshttps://doi.org/10.3390/diagnostics16203299openalex-snapshot
From Counting Cells to Reconstructing Death: A Commentary on Artificial Intelligence in Forensic Histopathology
inferred← citeshttps://doi.org/10.4018/979-8-2600-1358-8.ch007openalex-snapshot
AI in Veterinary Diagnostics
inferred← citeshttps://doi.org/10.70675/e30b336czb79bz4b87z8523z8b67d29d3815openalex-snapshot
Representation learning in Computational Pathology : Application to the Prediction of Molecular Cancer Features
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