转化式学习
个性化医疗
模式
计算机科学
数据科学
心理学
生物信息学
社会科学
社会学
生物
教育学
作者
Amalio Telenti,Michael Auli,Brian Hie,M. Cyrus Maher,Suchi Saria,John P. A. Ioannidis
摘要
Abstract Large language models (LLMs) are a type of machine learning model that learn statistical patterns over text, such as predicting the next words in a sequence of text. Both general purpose and task‐specific LLMs have demonstrated potential across diverse applications. Science and medicine have many data types that are highly suitable for LLMs, such as scientific texts (publications, patents and textbooks), electronic medical records, large databases of DNA and protein sequences and chemical compounds. Carefully validated systems that can understand and reason across all these modalities may maximize benefits. Despite the inevitable limitations and caveats of any new technology and some uncertainties specific to LLMs, LLMs have the potential to be transformative in science and medicine.
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