ArticleThe journal of pathology. Clinical research2024
Closing the loop - the role of pathologists in digital and computational pathology research.
Article in The journal of pathology. Clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Toward a regional digital pathology network in Tuscany: current status and implementation roadmap.Virchows Archiv : an international journal of pathology · 2026Article
- Confronting the known unknown: historical lessons and future strategies for Disease X.Frontiers in public health · 2026Review
- The diagnostic landscape of bone and soft tissue sarcoma: the pan-sarcoma era and AI-driven classification.The journal of pathology. Clinical research · 2025Article
- Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading.The journal of pathology. Clinical research · 2025Article
- The tumour histopathology "glossary" for AI developers.PLoS computational biology · 2025Review
- The value of morphology: osteoclast-like cells in soft tissue tumours.Pathology oncology research : POR · 2025Review
- Predicting lymph node recurrence in cT1-2N0 tongue squamous cell carcinoma: collaboration between artificial intelligence and pathologists.The journal of pathology. Clinical research · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists' annotations. There are three crucial elements to the pathologist's role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human-machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist's involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.
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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.