The emerging role of generative artificial intelligence in transplant medicine

被引：5

作者：

Deeb, Maya ^{[1
,2
]}

Gangadhar, Anirudh ^{[1
]}

Rabindranath, Madhumitha ^{[1
]}

Rao, Khyathi ^{[1
]}

Brudno, Michael ^{[3
]}

Sidhu, Aman ^{[1
]}

Wang, Bo ^{[3
]}

Bhat, Mamatha ^{[1
,2
]}

机构：

[1] Univ Hlth Network, Ajmera Transplant Program, Toronto, ON, Canada

[2] Univ Toronto, Dept Med, Div Gastroenterol & Hepatol, Toronto, ON, Canada

[3] Univ Hlth Network, DATA Team, Toronto, ON, Canada

来源：

AMERICAN JOURNAL OF TRANSPLANTATION | 2024年 / 24卷 / 10期

关键词：

artificial intelligence; deep learning; generative adversarial networks; large language models; machine learning; natural language processing; variational autoencoders; IMPACT;

D O I：

10.1016/j.ajt.2024.06.009

中图分类号：

R61 [外科手术学];

学科分类号：

摘要：

Generative artificial intelligence (AI), a subset of machine learning that creates new content based on training data, has witnessed tremendous advances in recent years. Practical applications have been identified in health care in general, and there is significant opportunity in transplant medicine for generative AI to simplify tasks in research, medical education, and clinical practice. In addition, patients stand to benefit from patient education that is more readily provided by generative AI applications. This review aims to catalyze the development and adoption of generative AI in transplantation by introducing basic AI and generative AI concepts to the transplant clinician and summarizing its current and potential applications within the field. We provide an overview of applications to the clinician, researcher, educator, and patient. We also highlight the challenges involved in bringing these applications to the bedside and need for ongoing refinement of generative AI applications to sustainably augment the transplantation field.

引用

页码：1724 / 1730

页数：7

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