Suppr超能文献

建立一个医学人工智能研究中心。

Developing a Research Center for Artificial Intelligence in Medicine.

作者信息

Langlotz Curtis P, Kim Johanna, Shah Nigam, Lungren Matthew P, Larson David B, Datta Somalee, Li Fei Fei, O'Hara Ruth, Montine Thomas J, Harrington Robert A, Gold Garry E

机构信息

Departments of Radiology, Medicine, and Biomedical Data Science, Center for Artificial Intelligence in Medicine and Imaging, Stanford University.

Center for Artificial Intelligence in Medicine and Imaging, Stanford University.

出版信息

Mayo Clin Proc Digit Health. 2024 Dec;2(4):677-686. doi: 10.1016/j.mcpdig.2024.07.005. Epub 2024 Oct 28.

Abstract

Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners. The Center for Artificial Intelligence in Medicine and Imaging uses the following 4 key tactics to support AI/ML research: project-based learning opportunities that build interdisciplinary collaboration; internal grant programs that catalyze extramural funding; infrastructure that facilitates the rapid creation of large multimodal AI-ready clinical data sets; and educational and open data programs that engage the broader research community. The center is based on the premise that foundational and applied research are not in tension but instead are complementary. Solving important biomedical problems with AI/ML requires high-quality foundational team science that incorporates the knowledge and expertise of clinicians, clinician scientists, computer scientists, and data scientists. As AI/ML becomes an essential component of research and clinical care, multidisciplinary centers of excellence in AI/ML will become a key part of the scholarly portfolio of academic medical centers and will provide a foundation for the responsible, ethical, and fair implementation of AI/ML systems.

摘要

人工智能(AI)和机器学习(ML)正在推动生物科学领域的创新,并且已经在影响医学学术和临床护理的关键要素。许多医学院校正在通过建立学术单位来利用这些新技术的潜力,以促进和推动人工智能/机器学习领域的研究与创新。在斯坦福大学,我们在学术领袖、临床部门、校外资助以及行业合作伙伴的支持下,开发了一个成功的人工智能/机器学习研究中心模式。医学与影像人工智能中心采用以下4种关键策略来支持人工智能/机器学习研究:基于项目的学习机会,以建立跨学科合作;内部资助计划,以催化校外资金;便于快速创建大型多模态人工智能就绪临床数据集的基础设施;以及吸引更广泛研究群体的教育和开放数据计划。该中心基于这样一个前提,即基础研究和应用研究并非相互矛盾,而是相辅相成的。利用人工智能/机器学习解决重要的生物医学问题需要高质量的基础团队科学,其中要纳入临床医生、临床科学家、计算机科学家和数据科学家的知识与专业技能。随着人工智能/机器学习成为研究和临床护理的重要组成部分,人工智能/机器学习多学科卓越中心将成为学术医疗中心学术组合的关键部分,并将为负责任、符合道德且公平地实施人工智能/机器学习系统奠定基础。

相似文献

1
Developing a Research Center for Artificial Intelligence in Medicine.建立一个医学人工智能研究中心。
Mayo Clin Proc Digit Health. 2024 Dec;2(4):677-686. doi: 10.1016/j.mcpdig.2024.07.005. Epub 2024 Oct 28.
6
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
8
Transforming the Future of Surgeon-Scientists.变革外科医生-科学家的未来。
Ann Surg. 2024 Feb 1;279(2):231-239. doi: 10.1097/SLA.0000000000006148. Epub 2023 Nov 2.
10
Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.利用眼科图像的人工智能基础考量。
Ophthalmology. 2022 Feb;129(2):e14-e32. doi: 10.1016/j.ophtha.2021.08.023. Epub 2021 Aug 31.

本文引用的文献

7
Multimodal biomedical AI.多模态生物医学人工智能。
Nat Med. 2022 Sep;28(9):1773-1784. doi: 10.1038/s41591-022-01981-2. Epub 2022 Sep 15.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验