Large language model use in clinical oncology
Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
23 October 2024
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| In: |
npj precision oncology
Year: 2024, Volume: 8, Pages: 1-17 |
| ISSN: | 2397-768X |
| DOI: | 10.1038/s41698-024-00733-4 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41698-024-00733-4 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41698-024-00733-4 
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| Author Notes: | Nicolas Carl, Franziska Schramm, Sarah Haggenmüller, Jakob Nikolas Kather, Martin J. Hetz, Christoph Wies, Maurice Stephan Michel, Frederik Wessels & Titus J. Brinker |
MARC
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| 520 | |a Large language models (LLMs) are undergoing intensive research for various healthcare domains. This systematic review and meta-analysis assesses current applications, methodologies, and the performance of LLMs in clinical oncology. A mixed-methods approach was used to extract, summarize, and compare methodological approaches and outcomes. This review includes 34 studies. LLMs are primarily evaluated on their ability to answer oncologic questions across various domains. The meta-analysis highlights a significant performance variance, influenced by diverse methodologies and evaluation criteria. Furthermore, differences in inherent model capabilities, prompting strategies, and oncological subdomains contribute to heterogeneity. The lack of use of standardized and LLM-specific reporting protocols leads to methodological disparities, which must be addressed to ensure comparability in LLM research and ultimately leverage the reliable integration of LLM technologies into clinical practice. | ||
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