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Updated: Jun 30, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Large language models and multimodal foundation models for precision oncology
Daniel Truhn1, Jan-Niklas Eckardt2,3, Dyke Ferber4,5
1Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany.
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.
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