The emerging role of generative artificial intelligence in transplant medicine

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...

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Vydané v:American journal of transplantation Ročník 24; číslo 10; s. 1724 - 1730
Hlavní autori: Deeb, Maya, Gangadhar, Anirudh, Rabindranath, Madhumitha, Rao, Khyathi, Brudno, Michael, Sidhu, Aman, Wang, Bo, Bhat, Mamatha
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States Elsevier Inc 01.10.2024
Predmet:
Artificial Intelligence
deep learning
generative adversarial networks
Humans
large language models
machine learning
natural language processing
Organ Transplantation
variational autoencoders
generative adversarial networks
GenAI
natural language processing
LLaMA
AI
DL
GPT
machine learning
LLM
artificial intelligence
EHR
deep learning
variational autoencoders
NLP
GAN
large language models
ML
ISSN:1600-6135, 1600-6143, 1600-6143
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
ObjectType-Review-3
content type line 23
ISSN:1600-6135
1600-6143
1600-6143
DOI:10.1016/j.ajt.2024.06.009