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

Research output: Contribution to journalArticlepeer-review

Tom Vercauteren, Mathias Unberath, Nicolas Padoy, Nassir Navab

Original languageEnglish
Article number8880624
Pages (from-to)198-214
Number of pages17
JournalProceedings of the IEEE
Volume108
Issue number1
Early online date23 Oct 2019
DOIs
Accepted/In press4 Oct 2019
E-pub ahead of print23 Oct 2019
Published1 Jan 2020

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King's Authors

Abstract

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.

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