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Israel
Published in Braithwaite Jeffrey, Mannion Russell, Matsuyama Yukihiro, Shekelle Paul, Whittaker Stuart, Al-Adawi Samir, Health Systems Improvement Across the Globe: Success Stories from 60 Countries, 2017
This inter-operability solution (initially provided by dbMotion Ltd., which was later bought by the U.S. company AllScripts) was enabled through detailed semantic mapping of clinical data that allowed data from disparate systems to be identified and compared. The software allows for a virtual patient record to be created in real-time at place of care. The efficacy of the system is limited by the fact that the data is in display-only form, rather than structured form (as discrete characters). While making clinical information available at the point of care improves decision-making, having the data in display-only form does not enable advanced features, such as decision support and safety alerts. However, the Ofek HIE program appears to have had a positive impact on quality and patient safety for both the ambulatory and the hospital system and has contributed to the lowering of costs.
State of the Art of Artificial Intelligence in Dentistry and Its Expected Future
Published in Lavanya Sharma, Mukesh Carpenter, Computer Vision and Internet of Things, 2022
Vukoman Jokanović, M. Živković, S. Živković
Augmented and artificial reality is mainly used in dental education to stimulate clinic practice and decrease the number of risks influenced by patient exercises. It creates conditions for exceptional improvement of the quality of feedback, which the virtual patient can offer to the students. At the same time, interactive communication between students and virtual assistants allows students to objectively evaluate their work, which creates conditions for high-quality training. Studies on the efficiency of these systems have shown that when using this method of learning, students achieve a significantly higher level of competencies in a shorter time than when using traditional simulation methods and aids [16].
Virtual reality for synergistic surgical training and data generation
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2021
Adnan Munawar, Zhaoshuo Li, Punit Kunjam, Nimesh Nagururu, Andy S. Ding, Peter Kazanzides, Thomas Looi, Francis X. Creighton, Russell H. Taylor, Mathias Unberath
The virtual patient model is obtained from a CT scan and is patient specific. We use an off-the-shelf CT segmentation pipeline (Ding et al. (2021)) to generate the corresponding segmented volume for each anatomical structure in 3D. The segmented volume is used for three purposes: 1) to texture each anatomy with its corresponding anatomical colour, 2) to differentiate skull and other critical anatomies for visual warnings, and 3) to generate 2D segmentation masks.