一个让放射科医生坐在AI创新驾驶座上的演示平台：一种方法论方法
An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: A Methodological Approach
摘要：
目前人工智能驱动的放射学研究需要资源和专业知识，而这些资源和专业知识往往是小型和资源有限的实验室无法获得的。能够参与人工智能研究的临床医生通常资金充足，人员充足，并且在人工智能和计算方面拥有丰富的经验，或者可以接触到这样的同事或设施。目前的成像数据是面向临床医生的，不容易接受机器学习计划，导致效率低下，耗时，成本高昂，依赖于一组数据工程师和机器学习科学家，并且经常妨碍放射科医生推动人工智能研究和创新。我们提出了我们开发的系统和方法，以满足基础设施和平台需求，同时减少人员和资源进入障碍。我们强调数据优先和模块化的方法，简化人工智能开发和部署过程，同时为放射科医生提供高效和熟悉的界面，使他们能够成为新的人工智能创新的驱动力。
英文摘要：
Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either have significant experience with AI and computing, or have access to colleagues or facilities that do. Current imaging data is clinician-oriented and is not easily amenable to machine learning initiatives, resulting in inefficient, time consuming, and costly efforts that rely upon a crew of data engineers and machine learning scientists, and all too often preclude radiologists from driving AI research and innovation. We present the system and methodology we have developed to address infrastructure and platform needs, while reducing the staffing and resource barriers to entry. We emphasize a data-first and modular approach that streamlines the AI development and deployment process while providing efficient and familiar interfaces for radiologists, such that they can be the drivers of new AI innovations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
