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CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.

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 interventio...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952279/
https://www.ncbi.nlm.nih.gov/pubmed/31920208
http://dx.doi.org/10.1109/JPROC.2019.2946993
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description 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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spelling pubmed-69522792020-01-09 CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions. Proc IEEE Inst Electr Electron Eng Article 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. IEEE 2019-10-23 /pmc/articles/PMC6952279/ /pubmed/31920208 http://dx.doi.org/10.1109/JPROC.2019.2946993 Text en https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title_full CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title_fullStr CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title_full_unstemmed CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title_short CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions: This article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-AI team of actors. In addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the OR or the IR suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
title_sort cai4cai: the rise of contextual artificial intelligence in computer-assisted interventions: this article overviews ideas as to how to incorporate the range of prior knowledge and instantaneous sensory information from experts, sensors and actuators for use in computer-assisted interventions, as well as learning how to develop a representation of the surgery or intervention among a mixed human-ai team of actors. in addition, the design of interventional systems and associated cognitive shared control schemes for online uncertainty awareness when making decisions in the or or the ir suite is discussed, and it is noted how this is critical for producing precise and reliable interventions.
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6952279/
https://www.ncbi.nlm.nih.gov/pubmed/31920208
http://dx.doi.org/10.1109/JPROC.2019.2946993
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