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Intelligence and autonomy in future Robotic surgery

  • Activ Surgical Inc.

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

6 Scopus citations

Abstract

Future surgery must create disruptive value for all stakeholders, namely, patients, surgeons, and healthcare systems. We envision that a significant part of future surgeries will be performed using intelligent, supervised, or fully autonomous technologies working collaboratively with human surgeons. The intelligence encompasses computer vision extending beyond the human visual spectrum, functional and physiologic tissue information beyond anatomy, connectivity to relevant surgical knowledge, and access to technical and clinical competence and proficiency at the point of care. Hence, the future of surgery delivers the best outcomes with minimal or no complications to everyone whenever and wherever surgery is needed. Today, although less than 1% of all surgery is performed using robot-assisted surgery (RAS), evidence is accruing that not only robot-assisted applications but also the incorporation of collaborative and supervised intelligence and autonomy will yield significant benefit in patient outcomes and safety. Our lives are surrounded and enhanced by technologies with collaborative and supervised semi-autonomous and fully autonomous control, from aviation to automobiles. With the ever-increasing digitalization of surgery, the similar convergence of data, hardware and software solutions in surgery will predictably embrace intelligence and autonomy in surgical tasks to enhance and improve surgical competence and proficiency. A seamless transition from initial collaborative and supervised RAS paradigms to an inevitable autonomous modality will usher in a new era where democratized optimal clinical outcomes and safety are accessible to all patients at the point of care.

Original languageEnglish
Title of host publicationRobotic Surgery
Subtitle of host publicationSecond Edition
PublisherSpringer International Publishing
Pages183-195
Number of pages13
ISBN (Electronic)9783030535940
ISBN (Print)9783030535933
DOIs
StatePublished - Apr 25 2021

Keywords

  • Collaborative autonomy
  • Computer vision
  • Convolutional neural network
  • Dexterity
  • Intelligence
  • Machine learning

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