Marcus, H.J. orcid.org/0000-0001-8000-392X, Ramirez, P.T., Khan, D.Z. orcid.org/0000-0001-9213-2550 et al. (79 more authors) (Cover date: January 2024) The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring. Nature Medicine, 30 (1). pp. 61-75. ISSN 1078-8956
Abstract
The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | IDEAL Robotics Colloquium; Humans; Robotics; Artificial Intelligence; Robotic Surgical Procedures |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Clinical Trials Research (LICTR) (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number NIHR National Inst Health Research NIHR302439 |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Apr 2024 15:10 |
Last Modified: | 09 Apr 2024 15:10 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1038/s41591-023-02732-7 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:211336 |