2. 背景(Background) 讨论了在医学挑战问题上的基础模型(Foundation Models),强调了之前的研究主要利用特定领域的训练。 提出了提问策略(Prompting Strategies),解释了如何通过提示来指导模型输出,并简要介绍了用于构建Medprompt方法的一些关键概念。 3. 实验设计(Experimental Design) 详细介绍了数据集和测试方法,确保测试...
GMAI, a class of advanced medical foundation models. ‘Generalist’ implies that they will be widely used across medical applications, largely replacing task-specific models. 通用医学人工智能(GMAI)模型的流程图。GMAI模型采用自监督学习等技术,基于多样化的医疗数据进行训练。为了便于灵活互动,模型能够将图像或...
The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI) models is likely to usher in newfound capabilities in medicine. We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMA
Generalist foundation models such as GPT-4 have displayed surprising capabilities in a wide variety of domains and tasks. Yet, there is a prevalent assumption that they cannot match specialist capabilities without intensive training of models with specialty knowledge...
GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health...
Traditional biomedical artificial intelligence (AI) models, designed for specific tasks or modalities, often exhibit limited flexibility in real-world deployment and struggle to utilize holistic information. Generalist AI holds the potential to address t
Foundationmodelsforgeneralistmedicalartificialintelligence /10.1038/s41586-023-05881-4 Received:3November2022 Accepted:22February2023 Publishedonline:12April2023 Checkforupdates MichaelMoor1,6,OishiBanerjee2,6,ZahraShakeriHosseinAbad3,HarlanM.Krumholz4,JureLeskovec1,EricJ.Topol5,7✉&PranavRajpurkar2,7✉...
20240612【可信基础模型】韩波:Exploring Trustworthy Foundation Models under Imperfect Data 2058 -- 34:41 App 20240103【大模型的可信之道:挑战、发展与机遇】仉尚航:迈向开放世界自动驾驶泛化感知 1809 -- 1:01:22 App 20240911【医疗人工智能的前沿进展】马骏:Towards Biomedical Image Segmentation Foundation Models...
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Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist mod...