ArticleNPJ digital medicine2024
Public evidence on AI products for digital pathology.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Considerations for digital pathology displays.Journal of pathology informatics · 2026Review
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs.Life (Basel, Switzerland) · 2026Review
- Accelerating the implementation of digital pathology in India.Journal of pathology informatics · 2026Review
- Regulatory science for AI-basedJournal of pathology informatics · 2026Article
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- Clinical artificial intelligence applications of vision-language foundation models.PLOS digital health · 2026Review
- An evaluation of artificial intelligence assisted prostate biopsy reporting in the Articulate Pro study.NPJ digital medicine · 2026Article
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- AI and the digital pathology revolution: clinical applications in cancer diagnosis and assessment.Expert review of molecular diagnostics · 2026Review
- From forensic 3D avatars to courtroom-grade digital twins: the next translational step for AI-assisted injury documentation.Forensic science, medicine, and pathology · 2026Article
- Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation.Virchows Archiv : an international journal of pathology · 2026Article
- Quantitative Texture Analysis of Cervical Cytology Identifies Endometrial Lesions in Atypical Glandular Cells on Liquid-Based Cytology: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Review
- Smart Lies and Sharp Eyes: Pragmatic Artificial Intelligence for Cancer Pathology: Promise, Pitfalls, and Access Pathways.Cancers · 2026Review
- Artificial intelligence in experimental and clinical in vitro analysis: applications, limitations, and future directions.Biotechnology notes (Amsterdam, Netherlands) · 2026Review
- Translational AI in whole-slide image cancer histopathology: state of the art and regulatory-approved solutions.Frontiers in digital health · 2026Review
- Article
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
Abstract
Novel products applying artificial intelligence (AI)-based methods to digital pathology images are touted to have many uses and benefits. However, publicly available information for products can be variable, with few sources of independent evidence. This review aimed to identify public evidence for AI-based products for digital pathology. Key features of products on the European Economic Area/Great Britain (EEA/GB) markets were examined, including their regulatory approval, intended use, and published validation studies. There were 26 AI-based products that met the inclusion criteria and, of these, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices. Only 10 of the products (38%) had peer-reviewed internal validation studies and 11 products (42%) had peer-reviewed external validation studies. To support transparency an online register was developed using identified public evidence ( https://osf.io/gb84r/ ), which we anticipate will provide an accessible resource on novel devices and support decision making.
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.