Revolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine
Kuo Zhang, Xiangbin Meng, Xiangyu Yan, Jiaming Ji, Jingqian Liu, Hua Xu, Heng Zhang, Da Liu 等 18 位
Chinese Academy of Medical Sciences & Peking Union Medical College Fu Wai Hospital State Key Laboratory of Cardiovascular Disease Peng Cheng Laboratory
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.
逐年被引趋势
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
生物医学Artificial Intelligence in Healthcare and Education
Machine Learning in Healthcare · Artificial Intelligence in Healthcare
参考文献 107
此处列出前 3 条
引用本文 126
按被引量排序,此处列出前 3 条