Post-pandemic Era: Robotic-Assisted Laparoscopic Surgery (Rals) in Urology

Dear Colleagues, 

Since its authorization by the FDA two decades ago, robotic assistance laparoscopic surgery (RALS) has become a more widely accepted surgery within urology due to its remote inoperative characteristics for both the surgeon and patient, which is attracting increasing attention from healthcare personnel following COVID-19 crisis. Frequent procedures using RALS focus on oncologic-related procedures for the kidneys, prostate and bladder, with the benefits of minimal invasion, less necessary blood transfusion and short length of stay generally documented by the healthcare econometrics. Unfortunately, the evidence of the medical merits of RALS in the past does not justify its cost. As a proposal to alleviate the coronavirus infection risk, however, it may offset this cost due to its minimal contact and remote operation. In this Special Issue, as we head towards the post pandemic era, we strive to explore the use of RALS in urology fields beyond simply the past 20 years. Today’s cutting-edge technology in modern medicine, including artificial intelligence (AI), AI medical imaging, online health data-base analysis, and remote patient monitoring (RPM), is becoming a leading force of multidisciplinary teamwork to make RALS procedure risk prognosis safer. Quality outcomes and comparative econometric cost effectiveness studies intend to establish exciting prospects for the challenge of RALS in an era of post pandemic healthcare.

The first instance of robotic surgery in the field was made more than 25 years ago; today, there are nearly a million instances of robotic surgery. Based on clinical evidence, there are numerous benefits afforded by robotics such as stability, accuracy, integration with modern imaging technology, greater range of motion, and telesurgery, which are absent in traditional surgical methods. Today, following the COVID-19 pandemic, remote robots may offer advances in development by fully utilizing the potential of such cutting-edge technologies as AI and 5G in digital medical imaging with remote operative procedures. To fortify surgical robot capability, it is important to minimize unnecessary patient contact to build towards a post-pandemic robotic surgery system. The new phase of remote robotic surgery in the future will not be limited to urology, but will potentially cover many other fields, including otolaryngology, neurosurgery, gynecology, endoscopy, as well as cardiothoracic, gastric, orthopedic, and miniature surgery, after which the post pandemic changes on the horizon can be evaluated. This Special Issue will enumerate major milestones and continue to focus on robotic assistance laparoscopic surgery (RALS). In addition, it invites subsequent articles on cutting-edge technologies in modern medicine, including AI medical image, artificial intelligence (AI), online health data-base analysis, and remote patient monitoring (RPM).

We aim to gather contributions from clinical RALS service outcomes and clinical researchers to examine the issues of new medical outcomes regarding quality, cost effectiveness, and the current technology of interdisciplinary AI, 5G, medical data science, and remote patient care to make urology surgery safer and more effective, hence promoting a translational and integrated approach to meet the post pandemic paradigm shift.

This Special Issue aims to provide a platform for authors, reviewers, and researchers around the world to present their discoveries and innovations, which serve in making RALS safer and more effective.

• The interdisciplinary robotic-assisted laparoscopic surgery (RALS).
• Quality outcomes and econometrics on RALS.
• Current technologies that can enhance RALS, such as AI, 5G, digital imaging, EMR, health data science, remote patient care, etc.

Dr. Chang-I Chen
Dr. Szu-Yuan Wu
Guest Editors

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• robotic assistance laparoscopic surgery (RALS)
• robotic econometrics
• quality outcomes
• onco-epidemiology
• cancer translational research
• cancer survivorship
• AI
• 5G
• digital imaging
• robotic health data analysis