Kowal, M.R. orcid.org/0000-0001-5628-4880, Ibrahim, M. orcid.org/0009-0007-3466-6790, Mihaljević, A.L. et al. (2 more authors) (2025) Technological Advances in Pre-Operative Planning. Journal of Clinical Medicine, 14 (15). 5385. ISSN: 2077-0383
Abstract
Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | robotic surgery; artificial intelligence in surgery; surgical training; AR in surgery; 3D imaging; surgical navigation; extended realities |
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) > Leeds Institute of Medical Research (LIMR) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 Aug 2025 13:51 |
Last Modified: | 01 Aug 2025 13:51 |
Status: | Published |
Publisher: | MDPI |
Identification Number: | 10.3390/jcm14155385 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:229925 |