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在放射学工作流程中实施人工智能算法：挑战与考量

Implementing Artificial Intelligence Algorithms in the Radiology Workflow: Challenges and Considerations.

作者信息

Korfiatis Panagiotis, Kline Timothy L, Meyer Holly M, Khalid Sana, Leiner Timothy, Loufek Brenna T, Blezek Daniel, Vidal David E, Hartman Robert P, Joppa Lori J, Missert Andrew D, Potretzke Theodora A, Taubel Jerome P, Tjelta Jason A, Callstrom Matthew R, Williamson Eric E

机构信息

Department of Radiology, Mayo Clinic, Rochester, MN.

Mayo Clinic Platform, Mayo Clinic, Rochester, MN.

出版信息

Mayo Clin Proc Digit Health. 2024 Dec 18;3(1):100188. doi: 10.1016/j.mcpdig.2024.100188. eCollection 2025 Mar.

DOI:10.1016/j.mcpdig.2024.100188
PMID:40207002
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11975811/
Abstract

Integration of AI-enabled algorithms into the radiology workflow presents a complex array of challenges that span operational, technical, clinical, and regulatory domains. Successfully overcoming these hurdles requires a multifaceted approach, including strategic planning, educational initiatives, and careful consideration of the practical implications for radiologists' workloads. Institutions must navigate these challenges with a clear understanding of the potential benefits and limitations of both vended and in-house developed AI tools.

摘要

将人工智能算法集成到放射学工作流程中带来了一系列复杂的挑战，这些挑战跨越了运营、技术、临床和监管领域。成功克服这些障碍需要采取多方面的方法，包括战略规划、教育举措，以及仔细考虑对放射科医生工作量的实际影响。各机构必须在清楚了解商用和内部开发的人工智能工具的潜在益处和局限性的情况下应对这些挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5210/11975811/01e6d14791d3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5210/11975811/98a3944274c0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5210/11975811/01e6d14791d3/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5210/11975811/98a3944274c0/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5210/11975811/01e6d14791d3/gr2.jpg

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