TY - GEN
T1 - A Strategic Roadmap for the Adoption and Operational Integration of Robot-Assisted Surgery in Modern Healthcare Systems
AU - Riad, Abdullah
AU - Hadid, Majed
AU - Padmanabhan, Regina
AU - Elomri, Adel
AU - El Omri, Abdelfatteh
AU - Aboumarzouk, Omar M.
AU - Al-Ansari, Abdulla
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Robot-assisted surgery (RAS) has witnessed a rapid expansion in recent years, further accelerated by the emergence of Healthcare 5.0 and its enabling technologies, particularly artificial intelligence. These advancements have significantly improved critical areas such as surgical training, image segmentation, and classification. Despite this growing adoption, healthcare institutions still lack a structured and comprehensive roadmap (RM) to guide the implementation and operational management of RAS (OM-RAS). This study aims to develop a robust RM to support healthcare facilities in effectively integrating RAS into clinical practice. Two main research questions are addressed: (1) What are the essential requirements for successful RAS implementation? and (2) What defines a practical and reliable framework for managing RAS operations? The contributions of this study are twofold: first, it offers a structured seven-theme roadmap for OM-RAS that centers technology and infrastructure (segmentation, kinematic modeling, AI tools, advanced control), links these with implementation, ethics–legal, and training/HR, and specifies initial integration steps aligned with Healthcare 4.0/5.0 (telesurgery, 6G, integrated AI, cybersecurity). Second, it sets an analytics-driven agenda that quantifies inter-theme dependencies to forecast workflow variability and optimize scheduling, resource allocation, and team assignment under real-world constraints.
AB - Robot-assisted surgery (RAS) has witnessed a rapid expansion in recent years, further accelerated by the emergence of Healthcare 5.0 and its enabling technologies, particularly artificial intelligence. These advancements have significantly improved critical areas such as surgical training, image segmentation, and classification. Despite this growing adoption, healthcare institutions still lack a structured and comprehensive roadmap (RM) to guide the implementation and operational management of RAS (OM-RAS). This study aims to develop a robust RM to support healthcare facilities in effectively integrating RAS into clinical practice. Two main research questions are addressed: (1) What are the essential requirements for successful RAS implementation? and (2) What defines a practical and reliable framework for managing RAS operations? The contributions of this study are twofold: first, it offers a structured seven-theme roadmap for OM-RAS that centers technology and infrastructure (segmentation, kinematic modeling, AI tools, advanced control), links these with implementation, ethics–legal, and training/HR, and specifies initial integration steps aligned with Healthcare 4.0/5.0 (telesurgery, 6G, integrated AI, cybersecurity). Second, it sets an analytics-driven agenda that quantifies inter-theme dependencies to forecast workflow variability and optimize scheduling, resource allocation, and team assignment under real-world constraints.
KW - Adoption
KW - Governance
KW - Healthcare 5.0
KW - Implementation Roadmap
KW - Operational Management
KW - Robot-Assisted Surgery
KW - Telesurgery
KW - Training
UR - https://www.scopus.com/pages/publications/105029676637
U2 - 10.1007/978-3-032-15576-4_19
DO - 10.1007/978-3-032-15576-4_19
M3 - Conference contribution
AN - SCOPUS:105029676637
SN - 9783032155757
T3 - Communications in Computer and Information Science
SP - 296
EP - 310
BT - Innovative Intelligent Industrial Production and Logistics - 6th IFAC/INSTICC International Conference, IN4PL 2025, Proceedings
A2 - Barata, José
A2 - Madani, Kurosh
A2 - Panetto, Hervé
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2025
Y2 - 23 October 2025 through 24 October 2025
ER -