The uses of robotics in surgery were hypothesized as far back as 1967, but it took nearly 30 years and the nation's largest agency, the Department of Defense, in conjunction with innovative startups and established research agencies to complete the first ...

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective.

Worldwide up to May 2022 there have been 515 million cases of COVID-19 infection and over 6 million deaths. The World Health Organization estimated that 115,000 healthcare workers died from COVID-19 from January 2020 to May 2021. This toll on human lives prompted this review on 5G based networking primarily on major components of healthcare delivery: diagnosis, patient monitoring, contact tracing, diagnostic imaging tests, vaccines distribution, emergency medical services, telesurgery and robot-assisted tele-ultrasound. The positive impact of 5G as core technology for COVID-19 applications enabled exchange of huge data sets in fangcang (cabin) hospitals and real-time contact tracing, while the low latency enhanced robot-assisted tele-ultrasound, and telementoring during ophthalmic surgery. In other instances, 5G provided a supportive technology for applications related to COVID-19, e.g., patient monitoring. The feasibility of 5G telesurgery was proven, albeit by a few studies on real patients, in very low samples size in most instances. The important future applications of 5G in healthcare include surveillance of elderly people, the immunosuppressed, and nano- oncology for Internet of Nano Things (IoNT). Issues remain and these require resolution before routine clinical adoption. These include infrastructure and coverage; health risks; security and privacy protection of patients’ data; 5G implementation with artificial intelligence, blockchain, and IoT; validation, patient acceptance and training of end-users on these technologies.

Large language models (LLMs) have the intrinsic potential to acquire medical knowledge. Several studies assessing LLMs on medical examinations have been published. However, there is no reported evidence on tests related to robot-assisted surgery. The aims of this study were to perform the first systematic review of LLMs on medical examinations and to establish whether ChatGPT, GPT-4, and Bard can pass the Fundamentals of Robotic Surgery (FRS) didactic test. A literature search was performed on PubMed, Web of Science, Scopus, and arXiv following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. A total of 45 studies were analyzed. GPT-4 passed several national qualifying examinations with questions in English, Chinese, and Japanese using zero-shot and few-shot learning. Med-PaLM 2 obtained similar scores on the United States Medical Licensing Examination with more refined prompt engineering techniques. Five different 2023 releases of ChatGPT, one of GPT-4, and one of Bard were tested on FRS. Seven attempts were performed with each release. The pass score was 79.5%. ChatGPT achieved a mean score of 64.6%, 65.6%, 75.0%, 78.9%, and 72.7% respectively from the first to the fifth tested release on FRS vs 91.5% of GPT-4 and 79.5% of Bard. GPT-4 outperformed ChatGPT and Bard in all corresponding attempts with a statistically significant difference for ChatGPT (p < 0.001), but not Bard (p = 0.002). Our findings agree with other studies included in this systematic review. We highlighted the potential and challenges of LLMs to transform the education of healthcare professionals in the different stages of learning, by assisting teachers in the preparation of teaching contents, and trainees in the acquisition of knowledge, up to becoming an assessment framework of leaners.

As robotic surgery emerged, there was an obvious need for structured training. There were previous published courses, however they were by single institutions focusing upon local needs. The Fundamentals of Robotic Surgery (FRS) is a basic technical skills curriculum (course) which was developed by all 11 specialties performing robotic surgery and validated in a multi-specialty, multi-institutional surgical trial. The FRS skills course was created using the Full Life Cycle Development method, with 4 Delphi consensus conferences consisting of representatives from all surgical specialties performing robotic surgery, who provided the course outcomes measures/metrics and content, from which a physical and virtual reality (VR) simulation models were created. Training and assessment occurred using Proficiency-based Progression (PBP) process with outcomes measures to quantitative metrics (benchmarks)—meaning a score that is based on experienced surgeons’ performance must be met before allowing the learner to progress to clinical surgery. The Validation Trial Design was a multi-institutional, multispecialty, single-blinded, parallel group randomized control trial. Transfer-of-training effectiveness was demonstrated by pre-test/post-test animal-parts model. The primary outcome measure (the course can accurately train and assess the technical performance of basic surgical skills) demonstrated the effectiveness of the PBP process of the FRS course. The secondary outcomes measure (learners performed equally well on the physical Dome, and the two computer simulated VR Domes compared to controls), indicating that performance on a simulator is equal to using physical models. Additional outcome measures demonstrated statistically significant better ‘quality of assessment’ was achieved by a binary checklist than using a global assessment Likert scale such as Global Evaluative Assessment of Robotic Surgery (GEARS) for training basic technical skills. The evidence from the evaluation of the quantitative assessment of performance to proficiency demonstrated effectiveness for training basic technical skills with the FRS standardized course for robotic surgery, prompting us to advocate that all surgical specialties’ training programs for robotic surgery should pass the FRS before progressing to advanced training and clinical robotic surgery. In addition, future robotic surgery course development should utilize the PBP process and templates.