在临床研究环境中整合人工智能工具:卵巢癌使用案例
Lorena Escudero Sanchez1,2,3, Thomas Buddenkotte1,4,5,6, Mohammad Al Sa'd3,7
1Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK.
Diagnostics (Basel, Switzerland)
|September 9, 2023
概括
这项研究引入了一个开源AI管道,将先进的放射性图像分析工具集成到临床研究中. 这弥合了人工智能开发和实际临床应用之间的差距,提高了诊断准确度.
科学领域:
- 医学成像和放射学医学成像和放射学
- 医疗保健中的人工智能
- 计算机视觉应用程序 计算机视觉应用
背景情况:
- 人工智能 (AI) 对医疗保健有很大的前景,特别是在提高预测准确性和减少医疗服务负担方面.
- 计算机视觉的进步正在彻底改变放射性图像分析,人工智能工具能够检测和划分瘤.
- 在技术部门开发人工智能工具和在临床环境中实际实施它们之间存在很大的差距.
研究的目的:
- 展示一个原型管道,将人工智能工具集成到临床研究环境中.
- 用开源软件弥合人工智能开发和临床应用之间的差距.
- 为放射科医生等最终用户提供可访问的平台,提供可视化工具.
主要方法:
- 开发一个完全使用开源软件的原型管道.
- 专注于简化来自AI社区的AI工具和模型的集成.
- 包括可视化应用程序用于最终用户与AI结果的交互.
主要成果:
- 一个功能性的原型管道已经成功演示.
- 该管道有助于整合人工智能驱动的放射学分析工具.
- 该平台是可访问的,并允许放射科医生与AI结果进行交互.
结论:
- 开发的开源管道有效地弥合了人工智能开发和临床研究之间的差距.
- 这种方法提高了放射科医生对AI工具的可访问性和可用性.
- 需要进一步努力将这些进展转化为常规的临床实践.
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