ReviewThe American journal of pathology2021
Ethics of AI in Pathology: Current Paradigms and Emerging Issues.
Review in The American journal of pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 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
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
36 citing papers in PubMed.
- Ethical guidelines for deploying artificial intelligence applications in the pathology field: Lessons learned from a prospective framework in a large tertiary care academic medical center.Journal of pathology informatics · 2026Article
- Artificial Intelligence for Tumor Tissue Detection in Stomach Cancer: A Retrospective Algorithm Development and Validation Study.Journal of clinical medicine · 2026Article
- Digital pathology and artificial intelligence in breast and gynecologic oncology: from molecular prediction to multimodal integration.Frontiers in oncology · 2026Review
- Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence.Frontiers in oncology · 2026Review
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- Practical implementation of AI in a non-academic, non-commercial Pathology laboratory: Real world experience and lessons learned.Histopathology · 2025Review
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Writing the Future: Artificial Intelligence, Handwriting, and Early Biomarkers for Parkinson's Disease Diagnosis and Monitoring.Biomedicines · 2025Review
- Advances and challenges in pathomics for liver cancer: From diagnosis to prognostic stratification.World journal of clinical oncology · 2025Review
- Artificial intelligence-enhanced interpretation of kidney transplant biopsy: focus on rejection.Current opinion in organ transplantation · 2025Review
- Artificial Intelligence in Placental Pathology: New Diagnostic Imaging Tools in Evolution and in Perspective.Journal of imaging · 2025Review
- Artificial intelligence in digital pathology - time for a reality check.Nature reviews. Clinical oncology · 2025Review
- Increasing the Impact and Value of Laboratory Medicine Through Effective and AI-Assisted Communication.EJIFCC · 2025Review
- Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections.Applied clinical informatics · 2025Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- Deep learning algorithm on H&E whole slide images to characterizeComputational and structural biotechnology journal · 2024Article
- The Promise of AI for Image-Driven Medicine: Qualitative Interview Study of Radiologists' and Pathologists' Perspectives.JMIR human factors · 2024Article
- Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives.Journal of imaging · 2024Review
- Scoping Review Shows the Dynamics and Complexities Inherent to the Notion of "Responsibility" in Artificial Intelligence within the Healthcare Context.Asian bioethics review · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Deep learning has rapidly advanced artificial intelligence (AI) and algorithmic decision-making (ADM) paradigms, affecting many traditional fields of medicine, including pathology, which is a heavily data-centric specialty of medicine. The structured nature of pathology data repositories makes it highly attractive to AI researchers to train deep learning models to improve health care delivery. Additionally, there are enormous financial incentives driving adoption of AI and ADM due to promise of increased efficiency of the health care delivery process. AI, if used unethically, may exacerbate existing inequities of health care, especially if not implemented correctly. There is an urgent need to harness the vast power of AI in an ethically and morally justifiable manner. This review explores the key issues involving AI ethics in pathology. Issues related to ethical design of pathology AI studies and the potential risks associated with implementation of AI and ADM within the pathology workflow are discussed. Three key foundational principles of ethical AI: transparency, accountability, and governance, are described in the context of pathology. The future practice of pathology must be guided by these principles. Pathologists should be aware of the potential of AI to deliver superlative health care and the ethical pitfalls associated with it. Finally, pathologists must have a seat at the table to drive future implementation of ethical AI in the practice of 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.