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Establishing priorities for implementation of large language models in pathology and laboratory medicine




Review

. 2024 Jan 11;11(1):100101.


doi: 10.1016/j.acpath.2023.100101.


eCollection 2024 Jan-Mar.

Affiliations

Item in Clipboard

Review

Simone Arvisais-Anhalt et al.


Acad Pathol.


.

Abstract

Artificial intelligence and machine learning have numerous applications in pathology and laboratory medicine. The release of ChatGPT prompted speculation regarding the potentially transformative role of large-language models (LLMs) in academic pathology, laboratory medicine, and pathology education. Because of the potential to improve LLMs over the upcoming years, pathology and laboratory medicine clinicians are encouraged to embrace this technology, identify pathways by which LLMs may support our missions in education, clinical practice, and research, participate in the refinement of AI modalities, and design user-friendly interfaces that integrate these tools into our most important workflows. Challenges regarding the use of LLMs, which have already received considerable attention in a general sense, are also reviewed herein within the context of the pathology field and are important to consider as LLM applications are identified and operationalized.


Keywords:

Artificial intelligence; GPT; Large language models (LLMs).

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.



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