ReviewThe journal of pathology. Clinical research2022
The ethical challenges of artificial intelligence-driven digital pathology.
Review in The journal of pathology. Clinical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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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.
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Who cites it
21 citing papers in PubMed.
- Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.Head and neck pathology · 2026Review
- The state of the art in artificial intelligence and digital pathology in prostate cancer.Nature reviews. Urology · 2026Review
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- The diagnostic landscape of bone and soft tissue sarcoma: the pan-sarcoma era and AI-driven classification.The journal of pathology. Clinical research · 2025Article
- Ethics of nursing in the digital age: perceptions and challenges among Korean nursing students.BMC medical ethics · 2025Article
- Use of quality checks and processes across digital histopathology: an initial survey from the Bigpicture consortium.Journal of clinical pathology · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Attitudes, Perceptions, and Factors Influencing the Adoption of AI in Health Care Among Medical Staff: Nationwide Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Improving the Annotation Process in Computational Pathology: A Pilot Study with Manual and Semi-automated Approaches on Consumer and Medical Grade Devices.Journal of imaging informatics in medicine · 2025Article
- PD-L1 Scoring Models for Non-Small Cell Lung Cancer in China: Current Status, AI-Assisted Solutions and Future Perspectives.Thoracic cancer · 2025Review
- A Conceptual Framework for Applying Ethical Principles of AI to Medical Practice.Bioengineering (Basel, Switzerland) · 2025Article
- Explainable AI for classifying vertebral fracture histology in digital spine pathology.Frontiers in medicine · 2025Article
- Deep learning algorithm on H&E whole slide images to characterizeComputational and structural biotechnology journal · 2024Article
- Potential Applications and Ethical Considerations for Artificial Intelligence in Traumatic Brain Injury Management.Biomedicines · 2024Review
- One label is all you need: Interpretable AI-enhanced histopathology for oncology.Seminars in cancer biology · 2023Review
- Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Cell projection plots: A novel visualization of bone marrow aspirate cytology.Journal of pathology informatics · 2023Article
- Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal.PloS one · 2023Article
- Inter- and intraobserver agreement in whole-slide digital ThinPrep samples of low-grade squamous lesions of the cervix uteri with known high-risk HPV status: A multicentric international study.Cancer cytopathology · 2022Article
- Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new possibilities for health care in recent years, particularly in the opportunities it brings for artificial intelligence (AI)-driven research. Recognising, however, that there is little scholarly debate on the ethics of digital pathology when used for AI research, this paper summarises what it sees as four key ethical issues to consider when deploying AI infrastructures in pathology, namely, privacy, choice, equity, and trust. The themes are inspired from the authors' experience grappling with the challenge of deploying an ethical digital pathology infrastructure to support AI research as part of the National Pathology Imaging Cooperative (NPIC), a collaborative of universities, hospital trusts, and industry partners largely located across the North of England. Though focusing on the UK case, internationally, few pathology departments have gone fully digital, and so the themes developed here offer a heuristic for ethical reflection for other departments currently making a similar transition or planning to do so in the future. We conclude by promoting the need for robust public governance mechanisms in AI-driven digital pathology.
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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.