ArticleVirchows Archiv : an international journal of pathology2026
The patient matters: a roundtable discussion on pathology in the era of digitization and AI.
Article in Virchows Archiv : an international journal of pathology, 2026. 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.
- Optimizing breast core needle biopsy biomarker throughput using an AI-based workflow.Histopathology · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology (DP) and artificial intelligence (AI) promise faster, more accurate cancer diagnostics, yet patient views remain undocumented. We explored perspectives of patient representatives on DP and AI implementation. A two-hour moderated roundtable with six Flemish cancer-patient advocates was recorded, transcribed and analyzed using reflexive thematic analysis. Participants anticipated improved accuracy, shorter turnaround times and stronger inter-laboratory collaboration. Trust in AI was high when algorithms were trained on diverse datasets and pathologists retained final responsibility. Clinical validity outweighed full algorithmic transparency, though ongoing explainability research was encouraged. Explicit mention of AI in reports was considered unnecessary if quality assurance was demonstrable. Privacy worries focused on potential insurer misuse rather than pseudonymized cloud transfer. Representatives requested future tools that translate technical reports into lay language and suggested questions to support shared decision-making. Patient representatives were generally supportive of the introduction of AI in pathology, provided that algorithms are clinically validated, trained on representative datasets, and deployed under clear professional oversight. Their comments specifically highlight expectations regarding human-AI collaboration, data governance, auditability, and communication about AI use. These AI-focused insights can help laboratories, vendors, and regulators align development and implementation with patient priorities.
Indexed as
Identifiers
41680538What 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.