• Arjun Jaggi
• Preetish Kakkar

This paper seeks to examine how the synergy between Gen-AI and AR can be used to improve the accuracy and results of surgeries. Gen-AI uses big data analysis to analyze large amounts of medical data to create predictive models that are crucial during surgical operations. At the same time, the visual information in AR is overlaid by patient-specific images so that surgeons can orient themselves in a complex topography more effectively. This literature review also presents prior uses of Gen-AI and AR in surgeries, as well as their use in preoperative planning, intraoperative navigation, and postoperative care, with multiple studies showing declines in surgical mistakes and complications. However, there are still areas for improvement in the literature, most notably the means by which these technologies are applied in clinical practice for a variety of surgical disciplines. The implementation plan, therefore, entails conducting clinical trials that are quantitative and matched with qualitative interviews in an attempt to evaluate the impact of Gen-AI and AR on the improvement of surgical effectiveness and patient safety. This research will seek to establish the role of these technologies in enhancing surgical operations so as to enhance the treatment of patients.

Real-time surgical guidance, Generative Artificial Intelligence, Augmented Reality, intraoperative imaging, surgical errors, patient outcomes, robotic surgery, predictive analytics, ethical consideration

"Gen-AI Enhanced AR for Surgical Precision: Revolutionizing Operative Procedures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 1, page no.45-54, January-2021, Available :http://www.jetir.org/papers/JETIR2101349.pdf

"Gen-AI Enhanced AR for Surgical Precision: Revolutionizing Operative Procedures", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 1, page no. pp45-54, January-2021, Available at : http://www.jetir.org/papers/JETIR2101349.pdf