ReviewFrontiers in medicine2019
Translational AI and Deep Learning in Diagnostic Pathology.
Review in Frontiers in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 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
116 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Machine learning versus conventional clinical methods in guiding management of heart failure patients-a systematic review.Heart failure reviews · 2021Pooled it
- Total product lifecycle regulatory considerations and recommendations for generative AI-enabled medical devices.European heart journal. Digital health · 2026Article
- Matching Patients With Cell Surface-Targeted Clinical Trials Using Large Language Models.JCO precision oncology · 2026Article
- Recent advances in corneal specular microscopy image analysis through artificial intelligence.PLOS digital health · 2026Review
- Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence.Biomedicines · 2026Article
- A two-stage deep learning framework for kidney disease detection using modified specular-free imaging and EfficientNetB2.Scientific reports · 2026Article
- PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.The American journal of pathology · 2026Article
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- Predicting lymph node metastasis in colorectal cancer using case-level multiple instance learning.World journal of gastroenterology · 2026Article
- Practical implementation of AI in a non-academic, non-commercial Pathology laboratory: Real world experience and lessons learned.Histopathology · 2025Review
- Use of quality checks and processes across digital histopathology: an initial survey from the Bigpicture consortium.Journal of clinical pathology · 2025Article
- Article
- Automated determination of tumor cell percentages in whole slide images: A nuclear classification study for molecular pathology tests.Journal of pathology informatics · 2025Article
- AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis.Diagnostic pathology · 2025Article
- Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.BMC gastroenterology · 2025Article
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
- An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images.Scientific reports · 2025Article
- Review
- Pathology Foundation Models.JMA journal · 2025Review
56 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.
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Identifiers
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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.