Generative Artificial Intelligence in Anatomic Pathology.
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...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 149; no. 4; pp. 298 - 319 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184219848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184219848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Apr2025 vid: 149 iid: 4 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 184219848 184219848 184219848 10.5858/arpa.2024-0215-RA 184219848 ppf: 298 ppct: 21 formats: fmt: @attributes: type: P tig: atl: Generative Artificial Intelligence in Anatomic Pathology. aug: au: Brodsky, Victor Ullah, Ehsan Bychkov, Andrey Song, Andrew H. Walk, Eric E. Louis, Peter Rasool, Ghulam Singh, Rajendra S. Mahmood, Faisal Bui, Marilyn M. Parwani, Anil V. affil: Department of Pathology and Immunology, Washington University School of Medicine in St Louis, St Louis, Missouri sug: subj: Artificial Intelligence, Generative Pathology, Clinical Methods Anatomy Workflow Diagnostic Errors Prevention and Control Education Standards Research Standards Automation Quality Control (Technology) Machine Learning Collaboration Ethics ab: 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. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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