论文标题

放射学界的AI认知助手的接受度:RSNA收集的数据报告

Receptivity of an AI Cognitive Assistant by the Radiology Community: A Report on Data Collected at RSNA

论文作者

Kanjaria, Karina, Pillai, Anup, Shivade, Chaitanya, Bendersky, Marina, Jadhav, Ashutosh, Mukherjee, Vandana, Syeda-Mahmood, Tanveer

论文摘要

由于机器学习和人工智能(AI)的进步,机器作为智能助手在其临床工作流程中成为智能助手的出现。但是,这些机器正在使用哪些系统的临床思维过程?它们是否足够与放射科医生的相似之处,以至于被信任的助手吗?在2016年北美放射学会(RSNA)的2016年科学大会和年度会议上进行了现场演示。该演示是以一种提问系统的形式提出的,该系统采用了放射学多项选择问题和医疗图像作为输入。然后,AI系统展示了认知工作流程,涉及文本分析,图像分析和推理,以处理问题并生成最可能的答案。经历了演示并测试问答系统的参与者提供了示威调查。在报道的54,037次会议注册人中,有2,927个参观了示范室,有1,991次经历了演示,而1,025人完成了一项示威后的调查。在本文中,显示了调查的方法,并提供了其结果的摘要。调查的结果表明,放射科医生对认知计算技术和人工智能的接受程度很高。

Due to advances in machine learning and artificial intelligence (AI), a new role is emerging for machines as intelligent assistants to radiologists in their clinical workflows. But what systematic clinical thought processes are these machines using? Are they similar enough to those of radiologists to be trusted as assistants? A live demonstration of such a technology was conducted at the 2016 Scientific Assembly and Annual Meeting of the Radiological Society of North America (RSNA). The demonstration was presented in the form of a question-answering system that took a radiology multiple choice question and a medical image as inputs. The AI system then demonstrated a cognitive workflow, involving text analysis, image analysis, and reasoning, to process the question and generate the most probable answer. A post demonstration survey was made available to the participants who experienced the demo and tested the question answering system. Of the reported 54,037 meeting registrants, 2,927 visited the demonstration booth, 1,991 experienced the demo, and 1,025 completed a post-demonstration survey. In this paper, the methodology of the survey is shown and a summary of its results are presented. The results of the survey show a very high level of receptiveness to cognitive computing technology and artificial intelligence among radiologists.

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