ReviewThe Journal of pathology2022
The state of the art for artificial intelligence in lung digital pathology.
Review in The Journal of pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
52 citing papers in PubMed.
- A deep learning framework for histopathological analysis of pixel-level extracellular matrix variation in standard H&E-stained images.Scientific reports · 2026Article
- Computational pathology features of immune architecture predict clinically relevant outcomes in small-cell lung cancer (SCLC).NPJ precision oncology · 2026Article
- Diagnosis of Lung Diseases in the Artificial Intelligence Era.International journal of molecular sciences · 2026Article
- Biomarkers in Invasive Pulmonary Fungal Infections: Where Do We Stand?Journal of fungi (Basel, Switzerland) · 2026Review
- The impact of AI on modern oncology from early detection to personalized cancer treatment.NPJ precision oncology · 2026Review
- Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.PLoS computational biology · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- Deep Learning-Based Segmentation of Lung Adenocarcinoma Whole-Slide Images for Objective Grading, Tumor Spread Through Air Spaces Identification, and Mutation Prediction.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025Article
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Clinician Perspectives on Digital and Computational Pathology: Clinical Benefits, Concerns, and Willingness to Adopt.Diagnostics (Basel, Switzerland) · 2025Article
- Heterogeneity of Cellular Senescence, Senotyping, and Targeting by Senolytics and Senomorphics in Lung Diseases.International journal of molecular sciences · 2025Review
- EGFR Mutation Detection in Whole Slide Images of Non-Small Cell Lung Cancers Using a Two-Stage Deep Transfer Learning Approach.Cancer medicine · 2025Article
- Collagen disorder architecture features are associated with clinical, molecular, genetic factors and survival outcomes in colon cancer.NPJ precision oncology · 2025Article
- Application of liquid biopsy in differentiating lung cancer from benign pulmonary nodules (Review).International journal of molecular medicine · 2025Review
- Comparative performance of PD-L1 scoring by pathologists and AI algorithms.Histopathology · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Segmentation of Non-Small Cell Lung Carcinomas: Introducing DRU-Net and Multi-Lens Distortion.Journal of imaging · 2025Article
- Computational analysis of whole slide images predicts PD-L1 expression and progression-free survival in immunotherapy-treated non-small cell lung cancer patients.Journal of translational medicine · 2025Article
- Statistical analysis of spatial patterns in tumor microenvironment images.Nature communications · 2025Article
- Thoracic Ultrasound for Pre-Procedural Dynamic Assessment of Non-Expandable Lung: A Non-Invasive, Real-Time and Multifaceted Diagnostic Tool.Journal of clinical medicine · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Registered trials
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.