Evaluating Use of Generative Artificial Intelligence in Clinical Pathology Practice: Opportunities and the Way Forward.

Autor:	McCaffrey P; From the Departments of Pathology (McCaffrey, Thaker) and Radiology (McCaffrey), University of Texas Medical Branch, Galveston., Jackups R; the Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri (Jackups, Zaydman)., Seheult J; the Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (Seheult)., Zaydman MA; the Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri (Jackups, Zaydman)., Balis U; the Department of Pathology, University of Michigan, Ann Arbor (Balis)., Thaker HM; From the Departments of Pathology (McCaffrey, Thaker) and Radiology (McCaffrey), University of Texas Medical Branch, Galveston., Rashidi H; Computational Pathology & AI Center of Excellence, University of Pittsburgh, School of Medicine & UPMC, Pittsburgh, Pennsylvania (Rashidi)., Gullapalli RR; the Department of Pathology, Department of Chemical and Biological Engineering, University of New Mexico, Albuquerque (Gullapalli).
Jazyk:	angličtina
Zdroj:	Archives of pathology & laboratory medicine [Arch Pathol Lab Med] 2024 Oct 10. Date of Electronic Publication: 2024 Oct 10.
DOI:	10.5858/arpa.2024-0208-RA
Abstrakt:	Context.—: Generative artificial intelligence (GAI) technologies are likely to dramatically impact health care workflows in clinical pathology (CP). Applications in CP include education, data mining, decision support, result summaries, and patient trend assessments. Objective.—: To review use cases of GAI in CP, with a particular focus on large language models. Specific examples are provided for the applications of GAI in the subspecialties of clinical chemistry, microbiology, hematopathology, and molecular diagnostics. Additionally, the review addresses potential pitfalls of GAI paradigms. Data Sources.—: Current literature on GAI in health care was reviewed broadly. The use case scenarios for each CP subspecialty review common data sources generated in each subspecialty. The potential for utilization of CP data in the GAI context was subsequently assessed, focusing on issues such as future reporting paradigms, impact on quality metrics, and potential for translational research activities. Conclusions.—: GAI is a powerful tool with the potential to revolutionize health care for patients and practitioners alike. However, GAI must be implemented with much caution considering various shortcomings of the technology such as biases, hallucinations, practical challenges of implementing GAI in existing CP workflows, and end-user acceptance. Human-in-the-loop models of GAI implementation have the potential to revolutionize CP by delivering deeper, meaningful insights into patient outcomes both at an individual and population level. Competing Interests: The authors have no relevant financial interest in the products or companies described in this article. (© 2024 College of American Pathologists.)
Databáze:	MEDLINE
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