论文标题
用于评估AI放射系统的标准化X光片框架和平台
A Standardized Radiograph-Agnostic Framework and Platform For Evaluating AI Radiological Systems
论文作者
论文摘要
放射学对于准确诊断疾病和评估治疗反应至关重要。然而，挑战在于全球放射科医生的短缺。为此，正在开发许多人工智能解决方案。但是，人工智能放射学解决方案面临的挑战是缺乏基准测定和评估标准，以及收集多种数据以真正评估此类系统能够概括和正确处理边缘案例的能力的困难。我们提出了一个射线照相平台和框架，该平台和框架将允许评估其在各种地理位置，性别和年龄段之间概括其概括其能力的任何人工智能放射线解决方案。
Radiology has been essential to accurately diagnosing diseases and assessing responses to treatment. The challenge however lies in the shortage of radiologists globally. As a response to this, a number of Artificial Intelligence solutions are being developed. The challenge Artificial Intelligence radiological solutions however face is the lack of a benchmarking and evaluation standard, and the difficulties of collecting diverse data to truly assess the ability of such systems to generalise and properly handle edge cases. We are proposing a radiograph-agnostic platform and framework that would allow any Artificial Intelligence radiological solution to be assessed on its ability to generalise across diverse geographical location, gender and age groups.
