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There are developments in the new paradigm that enhance human abilities more than AI to support, along with decision-making and surgery. The development of AI-based AR communication systems can reduce natural limitations, improve awareness so that it can minimize error, and improve the efficiency of movement. The AR-based AI communication has already proved to reduce surgery time and they verify the improved accuracy [35]. The communication assisted AR is applied in gynecologic surgery in the way of robotic tools to overcome the drawback in surgical skills. The communication-based and robotic-assisted tools reduce the human tremor so that the accuracy can be maximized.

The anatomical relationship that exists between healthy organs and pathologic was well understood by surgeons by exhibiting the preoperative available images. Particularly if the organs of interest are immobile, then AR-based surgeries are successfully implemented. The recent application of AR in improved myoma detection and fibroid mapping are very good examples [36]. Some other similar techniques are used in gynecologic oncology for the identification of sentinel lymph nodes that have reduced the morbidity incorporated with group lymphadenectomy [37]. The communication technologies associated with three-dimensional printing to create physical models for better visualization of organ configuration offers an AR, which is unrealizable through other traditional imaging techniques. With recent techniques, advanced communication-enabled 3D printers can emulate various tissue types [38]. Given the changes in myoma size, position, and length within a uterus, 3D printing of a uterus can help the surgeon come up with good prior operative planning. In this manner, communication technologies associated with AI and AR offer a great deal in helping gynecological surgeons.

We concluded that further research and application of VR and AR in the healthcare and communication technologies are necessary. The summary of above discussed article is given in Table 1.6.

Table 1.6 Impact of AI-driven augmented and virtual reality in healthcare.

Source	Subject matter	Methods proposed	Performance analysis
[30]	Clinical applications	Deep learning–based diagnosis	Detects metastases in hematoxylin and eosin–stained tissue sections of lymph nodes of women with breast cancerAchieves 95% CI using 3-layer CNN
[31]	Clinical applications -Radiology	Clinical decision-making using CNN	Achieves 20% improvement over sonographer readings after training with ultrasound images of left and right carotid artery from 203 patients.
[32–34]	Clinical applications -survival prediction	Probabilistic Neural NetworkMulti-layer PerceptronGene expression classifierSupport Vector MachineRadial Basis Neural NetworkK-means algorithm	Trained with 23 demographic, tumor-related parameters and selected perioperative data from 102 patients.PNN achieves high prediction ability with an accuracy of 0.892 and sensitivity of 0.975
[35]	Surgical Applications	Rotational matrix and translation vector algorithm to reduce the geometric error	Improves the video accuracy by 0.30–0.40 mm (in terms of overlay error)Enhances processing rate to 10–13 frames/sDepth perception is increased by 90–100 mm
[36–38]	Surgical Applications	Feasibility of laparoscopic Sentinel Lymph Node (SLN) staging	245 SLN nodes were removed out of 370 lymph nodes from 87 patients.