辐射瘤学的人工智能研究:临床医生的概念和方法实用指南
Frank J P Hoebers1,2,3, Leonard Wee3, Jirapat Likitlersuang1,2
1Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States.
BJR open
|November 25, 2024
概括
人工智能 (AI) 在辐射瘤学中提供了效率提升,但由于知识缺口,预测模型的临床采用滞后. 在AI开发方面培训临床医生对于信任和协作至关重要.
科学领域:
- 医学物理 医学物理
- 计算机科学 计算机科学
- 在瘤学瘤学.
背景情况:
- 人工智能 (AI) 在辐射瘤学方面表现有前途,目前的应用重点是提高效率,例如自动细分和治疗规划.
- 影响患者决策的预测性AI模型对于临床使用来说不太成熟,阻碍了更广泛的AI采用.
- 临床医生对人工智能开发过程的理解有限,这导致了对人工智能技术的怀疑和低信心.
研究的目的:
- 解决临床医师在AI开发和医学应用方面的培训未满足的需求.
- 改善数据科学家和辐射瘤学家之间的多学科合作.
- 通过增加知识和理解,增强临床医生对人工智能的接受和信任.
主要方法:
- 该论文概述了人工智能研究和开发的基本步骤.
- 它采用实践放射瘤学家的观点来确定实际考虑.
- 内容旨在为临床医生提供工具,以批判性地评估AI研究论文.
主要成果:
- 辐射瘤学的AI应用目前在效率驱动的任务中比在临床决策中更先进.
- 临床医生在AI开发方面存在重大知识差距,影响了信任和采用.
- 人工智能培训临床医生被认为是促进合作和提高人工智能接受度的关键.
结论:
- 有必要教育放射性瘤学家关于人工智能开发和应用,以促进信任和协作.
- 了解人工智能过程可以消除深度学习的神秘性,并解决"黑子"的感知问题.
- 这篇论文为临床医生提供了一个框架来评估人工智能研究,在实践中促进知情使用.
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