ReviewModern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc2022
Digital pathology and artificial intelligence in translational medicine and clinical practice.
Review in Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 284 papers, 2 of them syntheses that pooled it.
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
284 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Current status and trends of machine learning applied in clinical research of gastric cancer from 2004 to 2023: global bibliometric and visual analysis.Frontiers in oncology · 2025Pooled it
- Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis.Frontiers in oncology · 2024Pooled it
- Immunophenotyping by Spatial Biology.Methods in molecular biology (Clifton, N.J.) · 2027Article
- An interpretable deep learning framework for multiclass bone marrow cytomorphology classification using EfficientNet and post hoc visualization techniques.Journal of pathology informatics · 2026Article
- From traditional histopathology to virtual staining: deep learning approaches for assessing tissue lesions in farmed fish.Veterinary research communications · 2026Review
- Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review).Biomedical reports · 2026Review
- Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence.Virchows Archiv : an international journal of pathology · 2026Article
- Generalizability and reproducibility challenges in morphometric assessment of nuclear atypia in breast cancer.Breast cancer (Tokyo, Japan) · 2026Article
- SmartHisto: Bayesian active learning for histology images.PLoS computational biology · 2026Article
- Evaluating the Performance of LYDIA: An AI-Powered Assistant in the Detection of Metastatic Tumors to Optimize Clinical Workflows and Inform Soft Tissue Surgical Decision-Making.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.Liver research (Beijing, China) · 2026Review
- DeepPlaque: a scalable multimodal platform for Aβ pathology and cell analysis in Alzheimer's disease.EMBO molecular medicine · 2026Article
- Deep visual multi-omics profiling links morphology and molecular programs in clear cell renal cell carcinoma.EMBO molecular medicine · 2026Article
- Antibody-Drug Conjugates in Lung Cancer: Promise, Progress, and Persistent Challenges.Current issues in molecular biology · 2026Review
- Deep Learning-Assisted Quality Control of Histology Teaching Slides: Detection and Localization of Tissue Fold Artifacts in H&E-Stained Images.Bioengineering (Basel, Switzerland) · 2026Article
- Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.Life (Basel, Switzerland) · 2026Review
- Rethinking pathology image analysis through shuffling.npj biomedical innovations · 2026Article
- Multimodal Phasor Analysis for Digital Pathology: Quantitative Characterization of Liver Iron Overload.Chemical & biomedical imaging · 2026Article
- Research progress on precision targeted therapy strategies for breast cancer based on tumor microenvironment.iScience · 2026Review
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
224 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.
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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.