The inevitable transformation of medicine and research by large language models: The possibilities and pitfalls

作者：Yuanxu Gao, Daniel T. Baptista‐Hon, Kang Zhang · 发表于：MedComm – Future Medicine · 年份：2023 · DOI：10.1002/mef2.49 · 被引用次数：11 · 研究领域：Machine Learning in Healthcare、Topic Modeling、Artificial Intelligence in Healthcare and Education

Large language models (LLMs) often refer to artificial intelligence models that consist of extensive parameters and have the ability to understand and generate human-like language. They are typically developed in a self-supervised learning manner and are trained on large quantities of unlabeled text to learn patterns in language. LLMs were initially used in natural language processing (NLP), but they have since been extended to a variety of tasks like processing biological sequences and combining text with other modalities of data. LLMs have the potential to revolutionize the way we approach scientific research and medicine. For example, by leveraging their ability to understand and interpret vast quantities of text data, LLMs can provide insights and make predictions that would otherwise be impossible. In the medical domain, LLMs can be used to analyze immense electronic health records and improve communication between healthcare professionals and patients. For example, LLMs can be used to automate triage, medical coding, and clinical documentation, which can help to improve the accuracy and efficiency of these processes. They can also be used to improve NLP in medical chatbots and virtual assistants, allowing patients to interact with healthcare services more efficiently and effectively. They can also be used to process medical records and patient data, enabling better diagnoses and more personalized treatments. They can also be used to analyze clinical trial data and ident...