5G in Healthcare: From COVID-19 to Future Challenges.

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.

Breaking down the silos of artificial intelligence in surgery: glossary of terms.

BACKGROUND: The literature on artificial intelligence (AI) in surgery has advanced rapidly during the past few years. However, the published studies on AI are mostly reported by computer scientists using their own jargon which is unfamiliar to surgeons. METHODS: A literature search was conducted in using PubMed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The primary outcome of this review is to provide a glossary with definitions of the commonly used AI terms in surgery to improve their understanding by surgeons. RESULTS: One hundred ninety-five studies were included in this review, and 38 AI terms related to surgery were retrieved. Convolutional neural networks were the most frequently culled term by the search, accounting for 74 studies on AI in surgery, followed by classification task (n = 62), artificial neural networks (n = 53), and regression (n = 49). Then, the most frequent expressions were supervised learning (reported in 24 articles), support vector machine (SVM) in 21, and logistic regression in 16. The rest of the 38 terms was seldom mentioned. CONCLUSIONS: The proposed glossary can be used by several stakeholders. First and foremost, by residents and attending consultant surgeons, both having to understand the fundamentals of AI when reading such articles. Secondly, junior researchers at the start of their career in Surgical Data Science and thirdly experts working in the regulatory sections of companies involved in the AI Business Software as a Medical Device (SaMD) preparing documents for submission to the Food and Drug Administration (FDA) or other agencies for approval.

Expert Consensus Recommendations for Robotic Surgery Credentialing.

OBJECTIVE: To define criteria for robotic credentialing using expert consensus. BACKGROUND: A recent review of institutional robotic credentialing policies identified significant variability and determined current policies are largely inadequate to ensure surgeon proficiency and may threaten patient safety. METHODS: Twenty-eight national robotic surgery experts were invited to participate in a consensus conference. After review of available institutional policies and discussion, the group developed a 91 proposed criteria. Using a modified Delphi process the experts were asked to indicate their agreement with the proposed criteria in three electronic survey rounds after the conference. Criteria that achieved 80% or more in agreement (consensus) in all rounds were included in the final list. RESULTS: All experts agreed that there is a need for standardized robotic surgery credentialing criteria across institutions that promote surgeon proficiency. Forty-nine items reached consensus in the first round, 19 in the second, and 8 in the third for a total of 76 final items. Experts agreed that privileges should be granted based on video review of surgical performance and attainment of clearly defined objective proficiency benchmarks. Parameters for ongoing outcome monitoring were determined and recommendations for technical skills training, proctoring, and performance assessment were defined. CONCLUSIONS: Using a systematic approach, detailed credentialing criteria for robotic surgery were defined. implementation of these criteria uniformly across institutions will promote proficiency of robotic surgeons and has the potential to positively impact patient outcomes.

A systematic review on artificial intelligence in robot-assisted surgery.

BACKGROUND: Despite the extensive published literature on the significant potential of artificial intelligence (AI) there are no reports on its efficacy in improving patient safety in robot-assisted surgery (RAS). The purposes of this work are to systematically review the published literature on AI in RAS, and to identify and discuss current limitations and challenges. MATERIALS AND METHODS: A literature search was conducted on PubMed, Web of Science, Scopus, and IEEExplore according to PRISMA 2020 statement. Eligible articles were peer-review studies published in English language from January 1, 2016 to December 31, 2020. Amstar 2 was used for quality assessment. Risk of bias was evaluated with the Newcastle Ottawa Quality assessment tool. Data of the studies were visually presented in tables using SPIDER tool. RESULTS: Thirty-five publications, representing 3436 patients, met the search criteria and were included in the analysis. The selected reports concern: motion analysis (n = 17), urology (n = 12), gynecology (n = 1), other specialties (n = 1), training (n = 3), and tissue retraction (n = 1). Precision for surgical tools detection varied from 76.0% to 90.6%. Mean absolute error on prediction of urinary continence after robot-assisted radical prostatectomy (RARP) ranged from 85.9 to 134.7 days. Accuracy on prediction of length of stay after RARP was 88.5%. Accuracy on recognition of the next surgical task during robot-assisted partial nephrectomy (RAPN) achieved 75.7%. CONCLUSION: The reviewed studies were of low quality. The findings are limited by the small size of the datasets. Comparison between studies on the same topic was restricted due to algorithms and datasets heterogeneity. There is no proof that currently AI can identify the critical tasks of RAS operations, which determine patient outcome. There is an urgent need for studies on large datasets and external validation of the AI algorithms used. Furthermore, the results should be transparent and meaningful to surgeons, enabling them to inform patients in layman's words. REGISTRATION: Review Registry Unique Identifying Number: reviewregistry1225.

Scientific Literature and Evaluation Metrics: Impact Factor, Usage Metrics, and Altmetrics.

Evaluating the quality of a scientific article has proven to be an elusive task. The most widely used bibliometric value currently used for this purpose, the journal impact factor, was not originally designed to determine the quality of research in a scientific article. Nevertheless, it has morphed into a surrogate to delineate the quality of a journal and even to represent the quality of individual articles in that that journal. Early 21st century advances in computer technology have seen an explosive revolution in scientific publication that have included open access, online publication, and world-wide accessibility to these publications. These developments have made it obvious that more sophisticated tools are required to delimit the quality of material present in the scientific literature. Usage data, which is measured as the number of full-text downloads of a specific article, is just one new method to evaluate the source of the vast material available that can be leveraged to more fully evaluate the merit of scientific literature.

Demonstrating the effectiveness of the fundamentals of robotic surgery (FRS) curriculum on the RobotiX Mentor Virtual Reality Simulation Platform.

Fundamentals of robotic surgery (FRS) is a proficiency-based progression curriculum developed by robotic surgery experts from multiple specialty areas to address gaps in existing robotic surgery training curricula. The RobotiX Mentor is a virtual reality training platform for robotic surgery. Our aims were to determine if robotic surgery novices would demonstrate improved technical skills after completing FRS training on the RobotiX Mentor, and to compare the effectiveness of FRS across training platforms. An observational, pre-post design, multi-institutional rater-blinded trial was conducted at two American College of Surgeons Accredited Education Institutes-certified simulation centers. Robotic surgery novices (n = 20) were enrolled and trained to expert-derived benchmarks using FRS on the RobotiX Mentor. Participants' baseline skill was assessed before (pre-test) and after (post-test) training on an avian tissue model. Tests were video recorded and graded by blinded raters using the Global Evaluative Assessment of Robotic Skills (GEARS) and a 32-criteria psychomotor checklist. Post hoc comparisons were conducted against previously published comparator groups. On paired-samples T tests, participants demonstrated improved performance across all GEARS domains (p < 0.001 to p = 0.01) and for time (p < 0.001) and errors (p = 0.003) as measured by psychometric checklist. By ANOVA, improvement in novices' skill after FRS training on the RobotiX Mentor was not inferior to improvement reported after FRS training on previously published platforms. Completion of FRS on the RobotiX Mentor resulted in improved robotic surgery skills among novices, proving effectiveness of training. These data provide additional validity evidence for FRS and support use of the RobotiX Mentor for robotic surgery skill acquisition.

5G Use in Healthcare: The Future is Present.

BACKGROUND: Most healthcare providers are unaware of the extraordinary opportunities for implementation in healthcare which can be enabled by 5G wireless networks. 5G created enormous opportunities for a myriad of new technologies, resulting in an integrated through 5G 'ecosystem'. Although the new opportunities in healthcare are immense, medicine is slow to change, as manifest by the paucity of new, innovative applications based upon this ecosystem. Thus, emerges the need to "avoid technology surprise" - both laparoscopic and robotic assisted minimally invasive surgery were delayed for years because the surgical community was either unaware or unaccepting of a new technology. DATABASE: PubMed (Medline) and Scopus (Elsevier) databases were searched and all published studies regarding clinical applications of 5G were retrieved. From a total of 40 articles, 13 were finally included in our review. DISCUSSION: The important transformational properties of 5G communications and other innovative technologies are described and compared to healthcare needs, looking for opportunities, limitations, and challenges to implementation of 5G and the ecosystem it has spawned. Furthermore, the needs in the clinical applications, education and research in medicine and surgery, in addition to the administrative infrastructure are addressed. Additionally, we explore the nontechnical challenges, that either support or oppose this new healthcare renovation. Based upon proven advantages of these innovative technologies, current scientific evidence is analyzed for future trends for the transformation of healthcare. By providing awareness of these opportunities and their advantages for patients, it will be possible to decrease the prolonged timeframe for acceptance and implementation for patients.

Development and validation of metric-based-training to proficiency simulation curriculum for upper gastrointestinal endoscopy using a novel assessment checklist.

BACKGROUND/AIMS: : This study aimed to design a structured simulation training curriculum for upper endoscopy and validate a new assessment checklist. MATERIALS AND METHODS: A proficiency-based progression stepwise curriculum was developed consisting of didactic, technical and non-technical components using a virtual reality simulator (VRS). It focused on: scope navigation, anatomical landmarks identification, mucosal inspection, retro-flexion, pathology identification, and targeting biopsy. A total of 5 experienced and 10 novice endoscopists were recruited. All participants performed each of the selected modules twice, and mean and median performance were compared between the two groups. Novices pre-set level of proficiency was set as 2 standard deviations below the mean of experts. Performance was assessed using multiple-choice questions for knowledge, while validated simulator parameters incorporated into a novel checklist; Simulation Endoscopic Skill Assessment Score (SESAS) were used for technical skills. RESULTS: : The following VRS outcome measures have shown expert vs novice baseline discriminative ability: total procedure time, number of attempts for esophageal intubation and time in red-out. All novice trainees achieved the preset level of proficiency by the end of training. There were no statistically significant differences between experts' and trainees' rate of complications, landmarks identification and patient discomfort. SESAS checklist showed high degree of agreement with the VRS metrices (kappa = 0.83) and the previously validated direct observation of procedural skills tool (kappa = 0.90). CONCLUSION: : The Fundamentals of Gastrointestinal Endoscopy simulation training curriculum and its SESAS global assessment tool have been primarily validated and can serve as a valuable addition to the gastroenterology fellowship programs. Follow up study of trainee performance in workplaces is recommended for consequences validation.

Proving the Effectiveness of the Fundamentals of Robotic Surgery (FRS) Skills Curriculum: A Single-blinded, Multispecialty, Multi-institutional Randomized Control Trial.

OBJECTIVE: To demonstrate the noninferiority of the fundamentals of robotic surgery (FRS) skills curriculum over current training paradigms and identify an ideal training platform. SUMMARY BACKGROUND DATA: There is currently no validated, uniformly accepted curriculum for training in robotic surgery skills. METHODS: Single-blinded parallel-group randomized trial at 12 international American College of Surgeons (ACS) Accredited Education Institutes (AEI). Thirty-three robotic surgery experts and 123 inexperienced surgical trainees were enrolled between April 2015 and November 2016. Benchmarks (proficiency levels) on the 7 FRS Dome tasks were established based on expert performance. Participants were then randomly assigned to 4 training groups: Dome (n = 29), dV-Trainer (n = 30), and DVSS (n = 32) that trained to benchmarks and control (n = 32) that trained using locally available robotic skills curricula. The primary outcome was participant performance after training based on task errors and duration on 5 basic robotic tasks (knot tying, continuous suturing, cutting, dissection, and vessel coagulation) using an avian tissue model (transfer-test). Secondary outcomes included cognitive test scores, GEARS ratings, and robot familiarity checklist scores. RESULTS: All groups demonstrated significant performance improvement after skills training (P < 0.01). Participating residents and fellows performed tasks faster (DOME and DVSS groups) and with fewer errors than controls (DOME group; P < 0.01). Inter-rater reliability was high for the checklist scores (0.82-0.97) but moderate for GEARS ratings (0.40-0.67). CONCLUSIONS: We provide evidence of effectiveness for the FRS curriculum by demonstrating better performance of those trained following FRS compared with controls on a transfer test. We therefore argue for its implementation across training programs before surgeons apply these skills clinically.

Orsi Consensus Meeting on European Robotic Training (OCERT): Results from the First Multispecialty Consensus Meeting on Training in Robot-assisted Surgery.

To improve patient outcomes in robotic surgery, robotic training and education need to be modernised and augmented. The skills and performance levels of trainees need to be objectively assessed before they operate on real patients. The main goal of the first Orsi Consensus Meeting on European Robotic Training (OCERT) was to establish the opinions of experts from different scientific societies on standardised robotic training pathways and training methodology. After a 2-d consensus conference, 36 experts identified 23 key statements allotted to three themes: training standardisation pathways, validation metrics, and implementation prerequisites and certification. After two rounds of Delphi voting, consensus was obtained for 22 of 23 questions among these three categories. Participants agreed that societies should drive and support the implementation of benchmarked training using validated proficiency-based pathways. All courses should deliver an internationally agreed curriculum with performance standards, be accredited by universities/professional societies, and, trainees should receive a certificate approved by professional societies and/or universities after successful completion of the robotic training courses. This OCERT meeting established a basis for bringing surgical robotic training out of the operating room by seeking input and consensus across surgical specialties for an objective, validated, and standardised training programme with transparent, metric-based training outcomes. PATIENT SUMMARY: The Orsi Consensus Meeting on European Robotic Training (OCERT) is an international, multidisciplinary, Delphi-panel study of scientific societies and experts focused on training in robotic surgery. The panel achieved consensus that standardised international training pathways should be the basis for a structured, validated, replicable, and certified approach to implementation of robotic technology.

Utilising an Accelerated Delphi Process to Develop Guidance and Protocols for Telepresence Applications in Remote Robotic Surgery Training.

CONTEXT: The role of robot-assisted surgery continues to expand at a time when trainers and proctors have travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: To provide guidance on setting up and running an optimised telementoring service that can be integrated into current validated curricula. We define a standardised approach to training candidates in skill acquisition via telepresence technologies. We aim to describe an approach based on the current evidence and available technologies, and define the key elements within optimised telepresence services, by seeking consensus from an expert committee comprising key opinion leaders in training. EVIDENCE ACQUISITION: This project was carried out in phases: a systematic review of the current literature, a teleconference meeting, and then an initial survey were conducted based on the current evidence and expert opinion, and sent to the committee. Twenty-four experts in training, including clinicians, academics, and industry, contributed to the Delphi process. An accelerated Delphi process underwent three rounds and was completed within 72 h. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. Consensus opinion was defined as ≥80% agreement. EVIDENCE SYNTHESIS: There was 100% consensus regarding an urgent need for international agreement on guidance for optimised telepresence. Consensus was reached in multiple areas, including (1) infrastructure and functionality; (2) definitions and terminology; (3) protocols for training, communication, and safety issues; and (4) accountability including ethical and legal issues. The resulting formulated guidance showed good internal consistency among experts, with a Cronbach alpha of 0.90. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts for development and content validation of optimised telepresence services for robotic surgery training. This guidance lays the foundation for launching telepresence services in robotic surgery. This guidance will require further validation. PATIENT SUMMARY: Owing to travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic, development of remote training and support via telemedicine is becoming increasingly important. We report a key opinion leader consensus view on a standardised approach to telepresence.

Utilising the Delphi Process to Develop a Proficiency-based Progression Train-the-trainer Course for Robotic Surgery Training.

CONTEXT: As the role of robot-assisted surgery continues to expand, development of standardised and validated training programmes is becoming increasingly important. OBJECTIVE: To provide guidance on an optimised "train-the-trainer" (TTT) structured educational programme for surgical trainers, in which delegates learn a standardised approach to training candidates in skill acquisition. We aim to describe a TTT course for robotic surgery based on the current published literature and to define the key elements within a TTT course by seeking consensus from an expert committee formed of key opinion leaders in training. EVIDENCE ACQUISITION: The project was carried out in phases: a systematic review of the current evidence was conducted, a face-to-face meeting was held in Philadelphia, and then an initial survey was created based on the current literature and expert opinion and sent to the committee. Thirty-two experts in training, including clinicians, academics, and industry, contributed to the Delphi process. The Delphi process underwent three rounds of survey in total. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. Consensus opinion was defined as ≥80% agreement. EVIDENCE SYNTHESIS: There was 100% consensus that there was a need for a standardized TTT course in robotic surgery. A consensus was reached in multiple areas, including the following: (1) definitions and terminologies, (2) qualifications to attend, (3) course objectives, (4) precourse considerations, (5) requirement of e-learning, (6) theory and course content, and (7) measurement of outcomes and performance level verification. The resulting formulated curriculum showed good internal consistency among experts, with a Cronbach alpha of 0.90. CONCLUSIONS: Using the Delphi methodology, we achieved an international consensus among experts to develop and reach content validation for a standardised TTT curriculum for robotic surgery training. This defined content lays the foundation for developing a proficiency-based progression model for trainers in robotic surgery. This TTT curriculum will require further validation. PATIENT SUMMARY: As the role of robot-assisted surgery continues to expand, development of standardised and validated training programmes is becoming increasingly important. There is currently a lack of high-level evidence on how best to train trainers in robot-assisted surgery. We report a consensus view on a standardised "train-the trainer" curriculum focused on robotic surgery. It was formulated by training experts from the USA and Europe, combining current evidence for training with experts' knowledge of surgical training.

Origins of Robotic Surgery: From Skepticism to Standard of Care.

BACKGROUND AND OBJECTIVES: 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 fully functional multipurpose surgical robot. Currently, the most prominently available multipurpose robotic surgery system with US Food and Drug Administration approval is Intuitive Surgical Inc.'s da Vinci Surgical System, which is found in operating rooms across the globe. Although now ubiquitous for minimally invasive surgery, early surgical robot prototypes were specialty focused. Originally, multipurpose robotic systems were intended for long-distance trauma surgery in battlefield settings. While there were impressive feats of telesurgery, the marketable focus has veered from this goal. Initially developed through SRI International and Defense Advanced Research Projects Agency, surgical robotics reached private industry through two major competitors, who later merged. METHODS: A thorough search of PubMed, Clinical Key, EBSCO, Ovid, ProQuest, and industry manufacturers' websites yielded 62 relevant articles, of which 51 were evaluated in this review. CONCLUSION: We analyzed the literature and referred to primary sources by conducting interviews with present and historical leaders in the field to yield a detailed chronology of surgical robotics development. As minimally invasive robotic procedures are becoming the standard of care, it is crucial to comprehensively document their historical context and importance as an emerging and evolving discipline.

Outlier experienced surgeon's performances impact on benchmark for technical surgical skills training.

BACKGROUND: Training in medicine must move to an outcome-based approach. A proficiency-based progression outcome approach to training relies on a quantitative estimation of experienced operator performance. We aimed to develop a method for dealing with atypical expert performances in the quantitative definition of surgical proficiency. METHODS: In study one, 100 experienced laparoscopic surgeons' performances on virtual reality and box-trainer simulators were assessed for two similar laparoscopic tasks. In study two, 15 experienced surgeons and 16 trainee colorectal surgeons performed one simulated hand-assisted laparoscopic colorectal procedure. Performance scores of experienced surgeons in both studies were standardized (i.e. Z-scores) using the mean and standard deviations (SDs). Performances >1.96 SDs from the mean were excluded in proficiency definitions. RESULTS: In study one, 1-5% of surgeons' performances were excluded having performed significantly below their colleagues. Excluded surgeons made significantly fewer correct incisions (mean = 7 (SD = 2) versus 19.42 (SD = 4.6), P < 0.0001) and a greater proportion of incorrect incisions (mean = 45.71 (SD = 10.48) versus 5.25 (SD = 6.6), P < 0.0001). In study two, one experienced colorectal surgeon performance was >4 SDs for time to complete the procedure and >6 SDs for path length. After their exclusions, experienced surgeons' performances were significantly better than trainees for path length: P = 0.031 and for time: P = 0.002. CONCLUSION: Objectively assessed atypical expert performances were few. Z-score standardization identified them and produced a more robust quantitative definition of proficiency.

Measuring Surgical Skills in Simulation-based Training.

Simulation-based surgical skills training addresses several concerns associated with the traditional apprenticeship model, including patient safety, efficient acquisition of complex skills, and cost. The surgical specialties already recognize the advantages of surgical training using simulation, and simulation-based methods are appearing in surgical education and assessment for board certification. The necessity of simulation-based methods in surgical education along with valid, objective, standardized techniques for measuring learned skills using simulators has become apparent. The most commonly used surgical skill measurement techniques in simulation-based training include questionnaires and post-training surveys, objective structured assessment of technical skills and global rating scale of performance scoring systems, structured assessments using video recording, and motion tracking software. The literature shows that the application of many of these techniques varies based on investigator preference and the convenience of the technique. As simulators become more accepted as a teaching tool, techniques to measure skill proficiencies will need to be standardized nationally and internationally.

Retention of Skills After Simulation-based Training in Orthopaedic Surgery.

Simulation-based surgical skills training has become essential in orthopaedic practice because of concerns about patient safety and an increase in technically challenging procedures. Surgical skills training in specifically designed simulation laboratories allows practice of procedures in a risk-free environment before they are performed in the operating room. The transferability of acquired skills to performance with patients is the most effective measure of the predictive validity of simulation-based training. Retention of the skills transferred to clinical situations is also critical. However, evidence of simulation-based skill retention in the orthopaedic literature is limited, and concerns about sustainability exist. Solutions for skill decay include repeated practice of the tasks learned on simulators and reinforcement of areas that are sensitive to decline. Further research is required to determine the retention rates of surgical skills acquired in simulation-based training as well as the success of proposed solutions for skill decay.

A stepwise model for simulation-based curriculum development for clinical skills, a modification of the six-step approach.

BACKGROUND: Despite the rapid growth in the use of simulation in health professions education, courses vary considerably in quality. Many do not integrate efficiently into an overall school/program curriculum or conform to academic accreditation requirements. Moreover, some of the guidelines for simulation design are specialty specific. STUDY DESIGN: We designed a model that integrates best practices for effective simulation-based training and a modification of Kern et al.'s 6-step approach for curriculum development. We invited international simulation and health professions education experts to complete a questionnaire evaluating the model. We reviewed comments and suggested modifications from respondents and reached consensus on a revised version of the model. RESULTS: We recruited 17 simulation and education experts. They expressed a consensus on the seven proposed curricular steps: problem identification and general needs assessment, targeted needs assessment, goals and objectives, educational strategies, individual assessment/feedback, program evaluation, and implementation. We received several suggestions for descriptors that applied the steps to simulation, leading to some revisions in the model. CONCLUSION: We have developed a model that integrates principles of curriculum development and simulation design that is applicable across specialties. Its use could lead to high-quality simulation courses that integrate efficiently into an overall curriculum.