ReviewJournal of pathology informatics2022
Lost in digitization - A systematic review about the diagnostic test accuracy of digital pathology solutions.
Review 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 4 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
4 citing papers in PubMed, 14 citations in OpenAlex.
- Validation of Digital Cytology for Primary Diagnosis Across a Range of Specimen Types.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Article
- The logic of medical reasoning: toward an integrated inductive, deductive, and abductive approach to clinical practices.Philosophy, ethics, and humanities in medicine : PEHM · 2025Article
- Streamlining a Patchwork - Exploring the Challenges of Digital Transformation in Pathology: Ethnographic Study.Journal of medical Internet research · 2025Article
- Utility of artificial intelligence in a binary classification of soft tissue tumors.Journal of pathology informatics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Digital pathology solutions are increasingly implemented for primary diagnostics in departments of pathology around the world. This has sparked a growing engagement on validation studies to evaluate the diagnostic performance of whole slide imaging (WSI) regarding safety, reliability, and accuracy. The aim of this review was to evaluate the performance of digital pathology for diagnostic purposes compared to light microscopy (LM) in human pathology, based on validation studies designed to assess such technologies. Methods: In this systematic review based on PRISMA guidelines, we analyzed validation studies of WSI compared with LM. We included studies of diagnostic performance of WSI regarding diagnostic test accuracy (DTA) indicators, degree of overdiagnosis, diagnostic concordance, and observer variability as a secondary outcome. Overdiagnosis is (for example) detecting a pathological condition that will either not progress or progress very slowly. Thus, the patient will never get symptoms from this condition and the pathological condition will never be the cause of death. From a search comprising four databases: PubMed, EMBASE, Cochrane Library, and Web of Science, encompassing the period 2010-2021, we selected and screened 12 peer-reviewed articles that fulfilled our selection criteria. Risk of bias was conducted through QUADAS-2 tool, and data analysis and synthesis were performed in a qualitative format. Results: We found that diagnostic performance of WSI was not inferior to LM for DTA indicators, concordance, and observer variability. The degree of overdiagnosis was not explicitly reported in any of the studies, while the term itself was used in one study and could be implicitly calculated in another. Conclusion: WSI had an overall high diagnostic accuracy based on traditional accuracy measurements; however, the degree of overdiagnosis is unknown.
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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.