# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 4 Discussion  
This systematic review investigates the impact of AR, MR, and hybrid approaches on medical simulation and reveals that most of the selected studies (43%) combine MR and hybrid approaches. Most studies use simulation predominantly as a medical/surgical training tool addressing both technical and non-technical skills. Eight of them use the simulator also as an automatic assessment tool for the evaluation of the user performance. However, given the promising role of simulation in objective skill assessment, we believe that more research is needed to integrate such functionalities in AR, MR, and hybrid simulators. Furthermore, the review examines how the virtual and physical components are implemented. As for the virtual component, we analyzed the following core elements: the tracking approach for deriving the spatiotemporal relationship between the real and virtual worlds, the display technologies, and the implementation of software haptic feedback and AI techniques. Tracking methods commonly employed for simulators include the use of EM sensors, vision-based approaches (mainly with planar printable markers), and hybrid methods. The latter use marker-based procedures combined with marker-less or sensor-based methods. Concerning the visualization modality, most of the revised simulators use a traditional 2D display; six studies report the use of HMDs, and only two a hand-held display. As for haptic feedback, it is mostly obtained through actual interaction with a physical replica of the anatomy. However, two simulators integrate a commercial haptic interface. More specifically, in one study, interaction with the physical environment is enriched by virtual haptic feedback generated by means of commercial haptic interfaces used to interact with internal organs not reproduced in the mannequin. Actually, in the literature, AI algorithms are studied but not yet robustly integrated. Indeed, among all the studies analyzed, only four report the use of AI methods. The use of AI techniques is increasing in the development of surgical simulators, as the AI potential is huge for automatic performance evaluation, metrics extraction, simulation level adaptation to the trainee performance, and realistic force feedback implementation. Thus, we believe that this issue deserves future attention for the development of high performance AR, MR, and hybrid simulators. Among all the studies analyzed, 78% use custom-made simulators. The manufacturing process for most custom-made phantoms starts with extracting 3D models from CT images of real patients. The 3D model is turned into a tangible physical replica using 3D printing technologies and casting fabrication processes. Printing materials such as resin and ABS are used to reproduce rigid parts (i.e., bones), and they adequately approximate the mechanical behavior of the natural tissue. Silicone mixtures and polyurethane materials are used for the manufacturing of soft parts to mimic human tissue properties. Among the revised custom-made phantoms, two include both patient-specific and non-patient specific synthetic organs. In addition, some authors equip the phantoms with EM sensors to implement AR applications. Overall, the manufacturing materials used are extremely durable over time allowing the reusability of the phantom (where is it possible) and thus a reduction in training costs. Given the importance of physical interaction in the skill acquisition for surgery, it is essential in the future both to study deeply the most suitable materials to mimic soft tissues and bones and to validate the realism of the interaction between physical models and surgical instrumentation. Other aspects investigated by this review include the complexity of the medical scenario, the integration of methods for evaluating clinical performance, and the level of portability of the simulator. Despite the importance of the first two aspects on learning effectiveness, only a few studies have addressed these issues.