April 24, 2024Journal of the American Medical Informatics Association

Large language models for biomedicine: foundations, opportunities, challenges, and best practices

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Generative large language models offer state-of-the-art performance in natural language processing tasks, highlighting their versatility in biomedicine.
Significant advancements in computational efficiency and large datasets have enhanced the effectiveness of these models for various applications.
Assessment of key methodologies like prompt-tuning and reinforcement learning from human feedback reveals their importance in leveraging large language models successfully in biomedicine applications, showcasing innovation in the field and real-world utility for practitioners in healthcare settings. This exploration highlights opportunities and challenges ahead for continued advancement and integration of LLMs in healthcare contexts.

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Implication

This article analyzes the integration challenges and opportunities of generative large language models in biomedicine, indicating best practices for implementation.

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Sahoo et al. (2024) studied this question.

synapsesocial.com/papers/68e6dc0eb6db643587657d96 https://doi.org/10.1093/jamia/ocae074

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Implication

This article analyzes the integration challenges and opportunities of generative large language models in biomedicine, indicating best practices for implementation.

Large language models for biomedicine: foundations, opportunities, challenges, and best practices

Key Points

Discussion

Implication

Cite This Study

Large language models for biomedicine: foundations, opportunities, challenges, and best practices

Key Points

Discussion

Implication

Cite This Study

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