Data-driven computational approaches have evolved to enable extraction of information from medical images with reliability, accuracy, and speed, which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theaters are extremely complex and typically rely on poorly integrated intraoperative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer-assisted interventions, we highlight the crucial need to take the context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer-assisted intervention (CAI4CAI) arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors, and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human–AI actor team; and how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision-making ultimately producing more precise and reliable interventions.

中文翻译：

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CAI4CAI：计算机辅助干预中情境人工智能的兴起

数据驱动的计算方法已经发展到能够可靠、准确和快速地从医学图像中提取信息，这已经改变了它们在临床实践中的解释和利用。虽然在介入成像领域渴望类似的好处，但这种雄心受到更高的异质性的挑战。介入套件和手术室中的临床工作流程极其复杂，通常依赖于集成度低的术中设备、传感器和支持基础设施。回顾机器学习和人工智能在计算机辅助干预方面的一些最令人兴奋的发展，我们强调了考虑背景和人为因素以应对这些挑战的关键需求。用于计算机辅助干预的情境人工智能 (CAI4CAI) 是一个新兴的机会，可以进入更广泛的外科数据科学领域。CAI4CAI 正在解决的核心挑战包括如何整合来自专家、传感器和执行器的先验知识和瞬时感官信息；如何在混合的人与人工智能演员团队之间创建和传达手术的忠实且可操作的共享表示；以及如何为在线不确定性感知协作决策设计干预系统和相关的认知共享控制方案，最终产生更精确和可靠的干预。CAI4CAI 正在解决的核心挑战包括如何整合来自专家、传感器和执行器的先验知识和瞬时感官信息；如何在混合的人与人工智能演员团队之间创建和传达手术的忠实且可操作的共享表示；以及如何为在线不确定性感知协作决策设计干预系统和相关的认知共享控制方案，最终产生更精确和可靠的干预。CAI4CAI 正在解决的核心挑战包括如何整合来自专家、传感器和执行器的先验知识和瞬时感官信息；如何在混合的人与人工智能演员团队之间创建和传达手术的忠实且可操作的共享表示；以及如何为在线不确定性感知协作决策设计干预系统和相关的认知共享控制方案，最终产生更精确和可靠的干预。