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

Simone Arvisais-Anhalt; Steven L Gonias; Sara G Murray

doi:10.1016/j.acpath.2023.100101

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

Acad Pathol. 2024 Jan 11;11(1):100101. doi: 10.1016/j.acpath.2023.100101. eCollection 2024 Jan-Mar.

Authors

Simone Arvisais-Anhalt¹, Steven L Gonias², Sara G Murray³

Affiliations

¹ Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
² Department of Pathology, University of California San Diego, La Jolla, CA, USA.
³ Department of Medicine, University of California San Francisco, San Francisco, CA, USA.

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).

Publication types

Review