ArticleJournal of pathology informatics2022
The key to an effective AI-powered digital pathology: Establishing a symbiotic workflow between pathologists and machine.
Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed.
- Predicability of PD-L1 expression in cancer cells based solely on H&E-stained sections.Journal of pathology informatics · 2025Review
- Human-machine interaction in computational cancer pathology.ESMO real world data and digital oncology · 2024Review
- Combined expert-in-the-loop-random forest multiclass segmentation U-net based artificial intelligence model: evaluation of non-small cell lung cancer in fibrotic and non-fibrotic microenvironments.Journal of translational medicine · 2024Article
- Digital pathology implementation in cancer diagnostics: towards informed decision-making.Frontiers in digital health · 2024Review
- Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Consensus-Based Minimum Requirements for the Adoption of a Computational Pathology Algorithm for Tumor Budding in Colorectal Cancer: An Early Health Technology Assessment.JCO clinical cancer informaticsArticle
- Pathology in the artificial intelligence era: Guiding innovation and implementation to preserve human insight.Academic pathologyArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pathology is a fundamental element of modern medicine that determines the final diagnosis of medical conditions, leads medical decisions, and portrays the prognosis. Due to continuous improvements in AI capabilities (e.g., object recognition and image processing), intelligent systems are bound to play a key role in augmenting pathology research and clinical practices. Despite the pervasive deployment of computational approaches in similar fields such as radiology, there has been less success in integrating AI in clinical practices and histopathological diagnosis. This is partly due to the opacity of end-to-end AI systems, which raises issues of interoperability and accountability of medical practices. In this article, we draw on interactive machine learning to take advantage of AI in digital pathology to open the black box of AI and generate a more effective partnership between pathologists and AI systems based on the metaphors of parameterization and implicitization.
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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.