PT  - JOURNAL ARTICLE
AU  - Bradshaw, Tyler J.
AU  - Tie, Xin
AU  - Warner, Joshua
AU  - Hu, Junjie
AU  - Li, Quanzheng
AU  - Li, Xiang
TI  - Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians
AID  - 10.2967/jnumed.124.268072
DP  - 2025 Feb 01
TA  - Journal of Nuclear Medicine
PG  - 173--182
VI  - 66
IP  - 2
4099  - http://jnm.snmjournals.org/content/66/2/173.short
4100  - http://jnm.snmjournals.org/content/66/2/173.full
SO  - J Nucl Med2025 Feb 01; 66
AB  - Large language models (LLMs) are poised to have a disruptive impact on health care. Numerous studies have demonstrated promising applications of LLMs in medical imaging, and this number will grow as LLMs further evolve into large multimodal models (LMMs) capable of processing both text and images. Given the substantial roles that LLMs and LMMs will have in health care, it is important for physicians to understand the underlying principles of these technologies so they can use them more effectively and responsibly and help guide their development. This article explains the key concepts behind the development and application of LLMs, including token embeddings, transformer networks, self-supervised pretraining, fine-tuning, and others. It also describes the technical process of creating LMMs and discusses use cases for both LLMs and LMMs in medical imaging.