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

IF 1.2 Q3 PATHOLOGY

Academic Pathology Pub Date : 2024-01-01 DOI:10.1016/j.acpath.2023.100101

Simone Arvisais-Anhalt MD , Steven L. Gonias MD, PhD , Sara G. Murray MD, MAS

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引用次数: 0

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.

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确定在病理学和检验医学中实施大型语言模型的优先事项

人工智能和机器学习在病理学和检验医学中应用广泛。ChatGPT 的发布引发了人们对大型语言模型（LLM）在病理学学术、检验医学和病理学教育中的潜在变革作用的猜测。由于 LLM 有可能在未来几年内得到改进，因此我们鼓励病理学和检验医学临床医生接受这项技术，确定 LLM 可以支持我们在教育、临床实践和研究方面的任务的途径，参与人工智能模式的改进，并设计用户友好的界面，将这些工具集成到我们最重要的工作流程中。本文还结合病理学领域的情况，回顾了在使用 LLM 方面所面临的挑战，这些挑战在 LLM 应用的确定和操作化过程中非常重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Academic Pathology PATHOLOGY-

CiteScore

2.20

自引率

20.00%

发文量

审稿时长

15 weeks

期刊介绍： Academic Pathology is an open access journal sponsored by the Association of Pathology Chairs, established to give voice to the innovations in leadership and management of academic departments of Pathology. These innovations may have impact across the breadth of pathology and laboratory medicine practice. Academic Pathology addresses methods for improving patient care (clinical informatics, genomic testing and data management, lab automation, electronic health record integration, and annotate biorepositories); best practices in inter-professional clinical partnerships; innovative pedagogical approaches to medical education and educational program evaluation in pathology; models for training academic pathologists and advancing academic career development; administrative and organizational models supporting the discipline; and leadership development in academic medical centers, health systems, and other relevant venues. Intended authorship and audiences for Academic Pathology are international and reach beyond academic pathology itself, including but not limited to healthcare providers, educators, researchers, and policy-makers.