Generative Artificial Intelligence in Anatomic Pathology
Open accessAbstract
Context.—: Generative artificial intelligence (AI) has emerged as a transformative force in various fields, including anatomic pathology, where it offers the potential to significantly enhance diagnostic accuracy, workflow efficiency, and research capabilities. Objective.—: To explore the applications, benefits, and challenges of generative AI in anatomic pathology, with a focus on its impact on diagnostic processes, workflow efficiency, education, and research. Data Sources.—: A comprehensive review of current literature and recent advancements in the application of generative AI within anatomic pathology, categorized into unimodal and multimodal applications, and evaluated for clinical utility, ethical considerations, and future potential. Conclusions.—: Generative AI demonstrates significant promise in various domains of anatomic pathology, including diagnostic accuracy enhanced through AI-driven image analysis, virtual staining, and synthetic data generation; workflow efficiency, with potential for improvement by automating routine tasks, quality control, and reflex testing; education and research, facilitated by AI-generated educational content, synthetic histology images, and advanced data analysis methods; and clinical integration, with preliminary surveys indicating cautious optimism for nondiagnostic AI tasks and growing engagement in academic settings. Ethical and practical challenges require rigorous validation, prompt engineering, federated learning, and synthetic data generation to help ensure trustworthy, reliable, and unbiased AI applications. Generative AI can potentially revolutionize anatomic pathology, enhancing diagnostic accuracy, improving workflow efficiency, and advancing education and research. Successful integration into clinical practice will require continued interdisciplinary collaboration, careful validation, and adherence to ethical standards to ensure the benefits of AI are realized while maintaining the highest standards of patient care.
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Synthetic region-level captions can easily be generated with the generative AI model to provide a wealth of text guidance.
“The synthetic region-level captions can easily be generated with the generative AI model to provide a wealth of text guidance88,127.”
From A multimodal whole-slide foundation model for pathology (2025) · cites this paper as [127]
The Multimodal Applications section (page unavailable) says multimodal models can generate text from histology images and describes pathology image-caption pairs. The AI-Driven Workflow section (page unavailable) also proposes text describing regions of interest. Neither passage establishes that synthetic region-level captions are easily generated or provide a wealth of text guidance. (LLM, conf=0.78)
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Self-citation
- Authors
- Brodsky, V., Ullah, E., Bychkov, A., Song, A. H., Walk, E. E., Louis, P., Rasool, G., Singh, R. S., Mahmood, F., Bui, M. M., & Parwani, A. V.
- Affiliations
- Washington University in St. Louis (US)
- Counties Manukau District Health Board (NZ)
- Kameda Medical Center (JP)
- Brigham and Women's Hospital (US)
- PathAI (United States) (US)
- Rutgers, The State University of New Jersey (US)
- University of South Florida (US)
- Summit Medical Group (United States) (US)
- Moffitt Cancer Center (US)
- The Ohio State University (US)
- ORCID
- Venue
- Archives of Pathology & Laboratory Medicine
- Published
- 2025-01-21
- Volume
- 149
- Issue
- 4
- Pages
- 298-318
- ISSN
- 0003-9985 (ISSN-L)
- 1543-2165
- OpenAlex
- W4406666355
- Semantic Scholar
- 17e34c392460c419d75d13d1055c9dbd5b60733e
- Cited by
- 48
- References
- 234
- Citations by year
- 2026: 27 · 2025: 21
- FWCI
- 2.90
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Archives of Pathology & Laboratory Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, pathological anatomy, virtual staining, workflow efficiency, reflex testing, AI-generated educational content, synthetic data generation, federated learning, prompt engineering, AI validation, ethical considerations, clinical integration
- MeSH terms
- Generative Artificial Intelligence, Artificial Intelligence, Humans, Pathology, Pathology, Clinical, Workflow
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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