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

Vercauteren T, Unberath M, Padoy N, Navab N (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Proceedings of the IEEE Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 108

Pages Range: 198-214

Article Number: 8880624

Journal Issue: 1

DOI: 10.1109/JPROC.2019.2946993

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.

Involved external institutions

King’s College London GB United Kingdom (GB) Johns Hopkins University (JHU) US United States (USA) (US) Université de Strasbourg (UDS) FR France (FR) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Vercauteren, T., Unberath, M., Padoy, N., & Navab, N. (2020). CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions. Proceedings of the IEEE, 108(1), 198-214. https://doi.org/10.1109/JPROC.2019.2946993

MLA:

Vercauteren, Tom, et al. "CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions." Proceedings of the IEEE 108.1 (2020): 198-214.

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