CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions

TL;DR

This paper discusses the emerging role of contextual artificial intelligence in improving computer-assisted interventions by integrating diverse data sources, human factors, and shared decision-making to enhance precision and reliability in complex surgical environments.

Contribution

It introduces the concept of CAI4CAI, emphasizing the importance of context and human factors in AI-driven surgical systems, and outlines key challenges and opportunities in this field.

Findings

Highlighting the need for integrating prior knowledge and sensory data.

Proposing shared representations for human-AI collaboration.

Addressing online uncertainty-aware decision making.

Abstract

Data-driven computational approaches have evolved to enable extraction of information from medical images with a 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 theatres are extremely complex and typically rely on poorly integrated intra-operative 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 context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer assisted intervention, or 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; 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.