ReviewCancers2022
Deep Learning Approaches in Histopathology.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 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
34 citing papers in PubMed, 70 citations in OpenAlex.
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Awareness, Educational Needs, and Curriculum Preferences Regarding AI and Medical Big Data Education Among Clinical Medicine Undergraduates: Cross-Sectional Survey Study.JMIR formative research · 2026Article
- Enhancing colorectal cancer diagnosis with the ferroptosis marker GPX4 and serum biomarkers: A retrospective analysis and machine learning approach.Biochemistry and biophysics reports · 2026Article
- Staining normalization in histopathology: Method benchmarking using multicenter dataset.Scientific reports · 2026Article
- Compact deep learning models for colon histopathology focusing performance and generalization challenges.Scientific reports · 2026Article
- The New Era of Precision Oncology. Practicing Pathology in Low Resource Countries and Settings.International journal of general medicine · 2026Article
- AI-Driven Digital Pathology: Deep Learning and Multimodal Integration for Precision Oncology.International journal of molecular sciences · 2025Review
- VIM-Polyp: Multimodal Colon Polyp Dataset with Video, Histopathology, and Protein Expression.Scientific data · 2025Article
- Radiomics and Machine Learning in Early Detection of Oral Potentially Malignant Disorders.Journal of pharmacy & bioallied sciences · 2025Review
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Review
- Simplified Artificial Intelligence Terminology for Pathologists.Diagnostics (Basel, Switzerland) · 2025Review
- Diagnostic accuracy of artificial intelligence-based multi-spectrum analysis for molecular fingerprint detection of SARS-CoV-2.Medicine · 2025Observational
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology.Bioengineering (Basel, Switzerland) · 2025Article
- Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry.NPJ digital medicine · 2025Article
- Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning and Multidisciplinary Imaging in Pediatric Surgical Oncology: A Scoping Review.Cancer medicine · 2025Article
- Artificial Intelligence in Histopathology.Journal of pharmacy & bioallied sciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The revolution of artificial intelligence and its impacts on our daily life has led to tremendous interest in the field and its related subtypes: machine learning and deep learning. Scientists and developers have designed machine learning- and deep learning-based algorithms to perform various tasks related to tumor pathologies, such as tumor detection, classification, grading with variant stages, diagnostic forecasting, recognition of pathological attributes, pathogenesis, and genomic mutations. Pathologists are interested in artificial intelligence to improve the diagnosis precision impartiality and to minimize the workload combined with the time consumed, which affects the accuracy of the decision taken. Regrettably, there are already certain obstacles to overcome connected to artificial intelligence deployments, such as the applicability and validation of algorithms and computational technologies, in addition to the ability to train pathologists and doctors to use these machines and their willingness to accept the results. This review paper provides a survey of how machine learning and deep learning methods could be implemented into health care providers' routine tasks and the obstacles and opportunities for artificial intelligence application in tumor morphology.
Indexed as
Identifiers
What OpenQuestion holds
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.