■人工智能(AI)的快速发展开创了自然语言处理(NLP)的新纪元,像ChatGPT这样的大型语言模型(LLM)处于领先地位。本文探讨了人工智能的深刻影响,特别是LLM,在医学图像处理领域。目标是通过解决与手动图像解释相关的历史挑战,提供对AI在改善医疗保健方面的变革潜力的见解。
■从2013年至2023年在WebofScience和PubMed数据库上进行了全面的文献检索,重点研究了LLM在医学成像处理中的转变。还审查了arXiv数据库上的最新出版物。我们的搜索标准包括所有类型的文章,包括摘要,评论文章,信件,和社论。出版物的语言仅限于英语,以促进进一步的内容分析。
■评论显示,人工智能,由LLM驱动,通过简化解释过程彻底改变了医学图像处理,传统上的特点是时间密集的人工努力。人工智能对医疗质量和患者福祉的影响是巨大的。凭借其强大的交互性和多模态学习能力,LLM为增强医学图像处理的各个方面提供了巨大的潜力。此外,变压器架构,LLM的基础,在这个领域越来越突出。
■总而言之,这篇综述强调了人工智能的关键作用,尤其是LLM,推进医学图像处理。这些技术具有提高迁移学习效率的能力,集成多模态数据,促进临床互动,并优化医疗保健的成本效益。LLM在临床环境中的潜在应用是有希望的,对未来的研究有着深远的影响,临床实践,和医疗保健政策。人工智能在医学图像处理中的变革性影响是不可否认的,它的持续发展和实施有望重塑医疗保健格局。
UNASSIGNED: The rapid advancement of artificial intelligence (AI) has ushered in a new era in natural language processing (NLP), with large language models (LLMs) like ChatGPT leading the way. This paper explores the profound impact of AI, particularly LLMs, in the field of medical image processing. The objective is to provide insights into the transformative potential of AI in improving healthcare by addressing historical challenges associated with manual image interpretation.
UNASSIGNED: A comprehensive literature search was conducted on the Web of Science and PubMed databases from 2013 to 2023, focusing on the transformations of LLMs in Medical Imaging Processing. Recent publications on the arXiv database were also reviewed. Our search criteria included all types of articles, including abstracts,
review articles, letters, and editorials. The language of publications was restricted to English to facilitate further content analysis.
UNASSIGNED: The
review reveals that AI, driven by LLMs, has revolutionized medical image processing by streamlining the interpretation process, traditionally characterized by time-intensive manual efforts. AI\'s impact on medical care quality and patient well-being is substantial. With their robust interactivity and multimodal learning capabilities, LLMs offer immense potential for enhancing various aspects of medical image processing. Additionally, the Transformer architecture, foundational to LLMs, is gaining prominence in this domain.
UNASSIGNED: In conclusion, this
review underscores the pivotal role of AI, especially LLMs, in advancing medical image processing. These technologies have the capacity to enhance transfer learning efficiency, integrate multimodal data, facilitate clinical interactivity, and optimize cost-efficiency in healthcare. The potential applications of LLMs in clinical settings are promising, with far-reaching implications for future research, clinical practice, and healthcare policy. The transformative impact of AI in medical image processing is undeniable, and its continued development and implementation are poised to reshape the healthcare landscape for the better.