ReviewJournal of exercise rehabilitation2021
A literature review of quality, costs, process-associated with digital pathology.
Review in Journal of exercise rehabilitation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis.NPJ precision oncology · 2025Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology incorporates the acquisition, management, sharing, and interpretation of pathological information, including slides and data, in a digital environment. Digital slides are created using a scanning device to capture a high-resolution image on glass slides for analysis on a computer or a mobile device. Though digital pathology has drastically grown over the last 10 years and has created opportunities to support specialists, few have attempted to address its full-scale implementation in routine clinical practice. To incorporate new technologies in diagnostic processes, it is necessary to study their application, the value they provide to specialists, and their effects on improvements across the entire workflow, rather than studying a particular element. In this study, we aimed to identify what have the current digital pathology systems contributed to the pathological and diagnostic process. We retrieved articles published between 2010 and 2020 from the databases PubMed and Google Scholar. We explored how digital pathology systems can better utilize existing medical data and new technologies within the current diagnostic workflow. While the evidence concerning the efficacy and effectiveness of digital pathology is mounting, high-quality evidence regarding its impact on resource allocation and value for diagnosis is still needed to support clinical diagnosis and policy decision-making.
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.