Large language models and multimodal foundation models for precision oncology
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 ar...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
22 March 2024
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| In: |
npj precision oncology
Year: 2024, Volume: 8, Pages: 1-4 |
| ISSN: | 2397-768X |
| DOI: | 10.1038/s41698-024-00573-2 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41698-024-00573-2 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41698-024-00573-2 
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| Author Notes: | Daniel Truhn, Jan-Niklas Eckardt, Dyke Ferber & Jakob Nikolas Kather |
| Summary: | 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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| Item Description: | Gesehen am 22.08.2024 |
| Physical Description: | Online Resource |
| ISSN: | 2397-768X |
| DOI: | 10.1038/s41698-024-00573-2 |