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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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“The synthetic region-level captions can easily be generated with the generative AI model to provide a wealth of text guidance88,127.”
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Self-citation
Metadata
- 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
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