ReviewDiagnostic pathology2024
The current troubled state of the global pathology workforce: a concise review.
Review in Diagnostic pathology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.Pediatric surgery international · 2026Pooled it
- Artificial intelligence in nongynecologic cytology: A systematic review of current research and commercial tools.Cancer cytopathology · 2026Pooled it
- Article
- Current quality challenges in H&E preparation: The critical foundation for increased use of digital pathology and AI emergence.Virchows Archiv : an international journal of pathology · 2026Article
- Inflammatory Dermatoses: An Audit of Histopathology Reporting Quality and the Role of Clinicopathological Conference.Cureus · 2026Article
- High-sensitivity Raman spectroscopy for prostate cancer detection and tissue extraction guidance.Biomedical optics express · 2026Article
- Deep-Learning Solution Providing Molecular Marker Subtyping of Breast Cancer Whole Slide Images: Protocol for a UK Clinical Service Evaluation Study.JMIR research protocols · 2026Article
- Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex.Croatian medical journal · 2026Article
- An artificial intelligence solution for evaluation of prostate needle core biopsy specimens: impact in a non-specialist setting.Virchows Archiv : an international journal of pathology · 2026Article
- Artificial Intelligence in Dermatopathology: An Update and Review of the Current Literature.Diagnostics (Basel, Switzerland) · 2026Review
- Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides.ESMO real world data and digital oncology · 2026Article
- Intraoperative cytologic evaluation for ovarian tumors : Utility in low resource settings and a roadmap for adoption.Gynecologic oncology reports · 2026Review
- Pilot test for the semi-automated embedding in the pathology laboratory at the University and Hospital Trust of Padua.Virchows Archiv : an international journal of pathology · 2026Article
- Pathology Foundation Models: Evolution, Current Landscape, Challenges and Opportunities from a Technical and Clinical Perspective.Bioengineering (Basel, Switzerland) · 2026Review
- Crowdsourcing enables robust cell annotation for breast cancer pathology.Scientific reports · 2026Article
- Decoding (digital) histopathology: The building blocks for computational researchers.PLOS digital health · 2026Review
- Article
- An AI framework for automated quality control of paraffin block and slide consistency: a clinical evaluation and human-machine comparison study.Virchows Archiv : an international journal of pathology · 2026Article
- Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.Journal of pathology informatics · 2026Article
- Digital pathology platforms with integrated AI algorithms: a structured landscape analysis and recommendations for clinical implementation.Virchows Archiv : an international journal of pathology · 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The histopathology workforce is a cornerstone of cancer diagnostics and is essential to the delivery of cancer services and patient care. The workforce has been subject to significant pressures over recent years, and this review considers them in the UK and internationally. These pressures include declining pathologist numbers, the increasing age of the workforce, and greater workload volume and complexity. Forecasts of the workforce's future in numerous countries are also not favourable - although this is not universal. Some in the field suggest that the effects of these pressures are already coming to bear, such as the financial costs of the additional measures needed to maintain clinical services. There is also some evidence of a detrimental impact on service delivery, patient care and pathologists themselves. Various solutions have been considered, including increasing the number of training places, enhancing recruitment, shortening pathology training and establishing additional support roles within pathology departments. A few studies have examined the effect of some of these solutions. However, the broader extent of their implementation and impact, if any, remains to be determined. In this regard, it is critical that future endeavours should focus on gaining a better understanding of the benefits of implemented workforce solutions, as well as obtaining more detailed and updated pathology workforce numbers. With a concentrated effort in these areas, the future of the pathology workforce could become brighter in the face of the increased demands on its services.
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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.