ReviewDeutsches Arzteblatt international2021
Artificial Intelligence in Pathology.
Review in Deutsches Arzteblatt international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 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
44 citing papers in PubMed, 62 citations in OpenAlex.
- Physicians' perspectives on artificial intelligence in electrocardiography in clinical practice: a qualitative study.BMC medical ethics · 2026Article
- Accelerating the implementation of digital pathology in India.Journal of pathology informatics · 2026Review
- Human versus artificial intelligence in oral pathology diagnosis: a comparative study of ChatGPT, Grok, and MANUS.Scientific reports · 2026Article
- [Artificial intelligence in diagnostics-a pathology perspective].Pathologie (Heidelberg, Germany) · 2026Review
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial intelligence-assisted diagnosis and histopathological grading of bladder cancer: current status, challenges, and future directions.Frontiers in digital health · 2026Review
- Imaging and Diagnostic Tools for Cetacean Mammary Gland Assessment: Challenges and Future Directions for Marine Mammal Pathology, Medicine and Research.Journal of mammary gland biology and neoplasia · 2025Review
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- The AI-powered pathologist: A global survey mapping initial trends in AI adoption and outlook.Journal of pathology informatics · 2025Article
- Article
- Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery.Clinics and practice · 2025Review
- Artificial intelligence in pancreatic cancer histopathology and diagnostics - implications for clinical decisions and biomarker discovery?Cell division · 2025Review
- Digital and Artificial Intelligence-based Pathology: Not for Every Laboratory - A Mini-review on the Benefits and Pitfalls of Its Implementation.Journal of clinical and translational pathology · 2025Article
- Novel Blended Learning on Artificial Intelligence for Medical Students: Qualitative Interview Study.JMIR medical education · 2025Article
- QuPath Edu and OpenMicroanatomy: Open-source virtual microscopy tools for medical education.Journal of anatomy · 2025Article
- Using digital pathology to standardize and automate histological evaluations of environmental samples.Environmental toxicology and chemistry · 2025Review
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- AI assistance in aesthetic medicine-A consensus on objective medical standards.Journal of cosmetic dermatology · 2024Article
- Artificial Intelligence in Histopathology.Journal of pharmacy & bioallied sciences · 2024Review
- Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.Journal of pathology informatics · 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
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncreasing digitalization enables the use of artificial intelligence (AI) and machine learning in pathology. However, these technologies have only just begun to be implemented, and no randomized prospective trials have yet shown a benefit of AI-based diagnosis. In this review, we present current concepts, illustrate them with examples from representative publications, and discuss the possibilities and limitations of their use.
methodsThis article is based on the results of a search in PubMed for articles published between January 1950 and January 2020 containing the searching terms "artificial intelligence," "deep learning," and "digital pathology," as well as the authors' own research findings.
resultsCurrent research on AI in pathology focuses on supporting routine diagnosis and on prognostication, particularly for patients with cancer. Initial data indicate that pathologists can arrive at a diagnosis faster and more accurately with the aid of a computer. In a pilot study on the diagnosis of breast cancer, involving 70 patients, sensitivity for the detection of micrometastases rose from 83.3% (by a pathologist alone) to 91.2% (by a pathologist combined with a computer algorithm). The evidence likewise suggests that AI applied to histomorphological properties of cells during microscopy may enable the inference of certain genetic properties, such as mutations in key genes and deoxyribonucleic acid (DNA) methylation profiles.
conclusionInitial proof-of-concept studies for AI in pathology are now available. Randomized, prospective studies are now needed so that these early findings can be confirmed or falsified.
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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.