JOURNAL ARTICLE

Large language models and multimodal foundation models for precision oncology

Daniel TruhnJan‐Niklas EckardtDyke FerberJakob Nikolas Kather

Year: 2024 Journal:   npj Precision Oncology Vol: 8 (1)Pages: 72-72   Publisher: Nature Portfolio

Abstract

Abstract 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.

Keywords:
Computer science Artificial neural network Paradigm shift Artificial intelligence Data science

Metrics

43
Cited By
35.22
FWCI (Field Weighted Citation Impact)
29
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Radiomics and Machine Learning in Medical Imaging
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging
Artificial Intelligence in Healthcare and Education
Health Sciences →  Medicine →  Health Informatics
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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