TY - JOUR
T1 - Generative AI and large language models in nuclear medicine
T2 - current status and future prospects
AU - Hirata, Kenji
AU - Matsui, Yusuke
AU - Yamada, Akira
AU - Fujioka, Tomoyuki
AU - Yanagawa, Masahiro
AU - Nakaura, Takeshi
AU - Ito, Rintaro
AU - Ueda, Daiju
AU - Fujita, Shohei
AU - Tatsugami, Fuminari
AU - Fushimi, Yasutaka
AU - Tsuboyama, Takahiro
AU - Kamagata, Koji
AU - Nozaki, Taiki
AU - Fujima, Noriyuki
AU - Kawamura, Mariko
AU - Naganawa, Shinji
N1 - Publisher Copyright:
© The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine 2024.
PY - 2024/11
Y1 - 2024/11
N2 - This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.
AB - This review explores the potential applications of Large Language Models (LLMs) in nuclear medicine, especially nuclear medicine examinations such as PET and SPECT, reviewing recent advancements in both fields. Despite the rapid adoption of LLMs in various medical specialties, their integration into nuclear medicine has not yet been sufficiently explored. We first discuss the latest developments in nuclear medicine, including new radiopharmaceuticals, imaging techniques, and clinical applications. We then analyze how LLMs are being utilized in radiology, particularly in report generation, image interpretation, and medical education. We highlight the potential of LLMs to enhance nuclear medicine practices, such as improving report structuring, assisting in diagnosis, and facilitating research. However, challenges remain, including the need for improved reliability, explainability, and bias reduction in LLMs. The review also addresses the ethical considerations and potential limitations of AI in healthcare. In conclusion, LLMs have significant potential to transform existing frameworks in nuclear medicine, making it a critical area for future research and development.
KW - Education
KW - Generative AI
KW - Large language model
KW - Nuclear medicine
KW - PET
KW - Report generation
KW - Report structuring
KW - SPECT
UR - http://www.scopus.com/inward/record.url?scp=85205056689&partnerID=8YFLogxK
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U2 - 10.1007/s12149-024-01981-x
DO - 10.1007/s12149-024-01981-x
M3 - Review article
C2 - 39320419
AN - SCOPUS:85205056689
SN - 0914-7187
VL - 38
SP - 853
EP - 864
JO - Annals of nuclear medicine
JF - Annals of nuclear medicine
IS - 11
ER -