Large language models and multimodal foundation models for precision oncology

被引:7
|
作者
Truhn, Daniel [1 ]
Eckardt, Jan-Niklas [2 ,3 ]
Ferber, Dyke [4 ,5 ]
Kather, Jakob Nikolas [2 ,3 ,4 ,5 ]
机构
[1] Univ Hosp Aachen, Dept Diagnost & Intervent Radiol, Aachen, Germany
[2] Tech Univ Dresden, Univ Hosp Carl Gustav Carus, Dept Internal Med 1, Dresden, Germany
[3] Tech Univ Dresden, Else Kroener Fresenius Ctr Digital Hlth, Dresden, Germany
[4] Univ Heidelberg Hosp, Natl Ctr Tumor Dis NCT, Heidelberg, Germany
[5] Heidelberg Univ Hosp, Dept Med Oncol, Heidelberg, Germany
关键词
ARTIFICIAL-INTELLIGENCE; CANCER;
D O I
10.1038/s41698-024-00573-2
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
The technological progress in artificial intelligence (AI) has massively accelerated since 2022, with far-reaching implications for oncology and cancer research. Large language models (LLMs) now perform at human-level competency in text processing. Notably, both text and image processing networks are increasingly based on transformer neural networks. This convergence enables the development of multimodal AI models that take diverse types of data as an input simultaneously, marking a qualitative shift from specialized niche models which were prevalent in the 2010s. This editorial summarizes these developments, which are expected to impact precision oncology in the coming years.
引用
收藏
页数:4
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