The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

At a time of significant global unrest and uncertainty surrounding how the delivery of clinical training will unfold over the coming years, we offer a systematic review, meta-analysis, and bibliometric analysis of global studies showing the crucial role simulation will play in training. Our aim was to determine the types of simulators in use, their effectiveness in improving clinical skills, and whether we have reached a point of global acceptance. A PRISMA-guided global systematic review of the neurosurgical simulators available, a meta-analysis of their effectiveness, and an extended analysis of their progressive scholarly acceptance on studies meeting our inclusion criteria of simulation in neurosurgical education were performed. Improvement in procedural knowledge and technical skills was evaluated. Of the identified 7405 studies, 56 studies met the inclusion criteria, collectively reporting 50 simulator types ranging from cadaveric, low-fidelity, and part-task to virtual reality (VR) simulators. In all, 32 studies were included in the meta-analysis, including 7 randomised controlled trials. A random effects, ratio of means effects measure quantified statistically significant improvement in procedural knowledge by 50.2% (ES 0.502; CI 0.355; 0.649,p< 0.001), technical skill including accuracy by 32.5% (ES 0.325; CI − 0.482; − 0.167,p< 0.001), and speed by 25% (ES − 0.25, CI − 0.399; − 0.107,p< 0.001). The initial number of VR studies (n= 91) was approximately double the number of refining studies (n= 45) indicating it is yet to reach progressive scholarly acceptance. There is strong evidence for a beneficial impact of adopting simulation in the improvement of procedural knowledge and technical skill. We show a growing trend towards the adoption of neurosurgical simulators, although we have not fully gained progressive scholarly acceptance for VR-based simulation technologies in neurosurgical education.

Stereotactic brain biopsy represents one of the earliest applications of surgical robotics. The aim of the present systematic review and bibliometric analysis was to evaluate the literature supporting robot-assisted brain biopsy and the extent to which the scientific community has accepted the technique.

Objective: To extend the IDEAL framework for device innovation, IDEAL-D, to include the preclinical stage of development (stage 0). Background: In previous work, the IDEAL collaboration has proposed frameworks for new surgical techniques and complex therapeutic technologies, the central tenet being that development and evaluation can and should proceed together in an ordered and logical manner that balances innovation and safety. Methods: Following agreement at the IDEAL Collaboration Council, a multidisciplinary working group was formed comprising 12 representatives from healthcare, academia, industry, and a patient advocate. The group conducted a series of discussions following the principles used in the development of the original IDEAL framework. Importantly, IDEAL aims for maximal transparency, optimal validity in the evaluation of primary effects, and minimization of potential risk to patients or others. The proposals were subjected to further review and editing by members of the IDEAL Council before a final consensus version was adopted. Results: In considering which studies are required before a first-in-human study, we have: (1) classified devices according to what they do and the risks they carry, (2) classified studies according to what they show about the device, and (3) made recommendations based on the principle that the more invasive and high risk a device is, the greater proof required of their safety and effectiveness before progression to clinical studies (stage 1). Conclusions: The proposed recommendations for preclinical evaluation of medical devices represent a proportionate and pragmatic approach that balances the de-risking of first-in-human translational studies against the benefits of rapid translation of new devices into clinical practice.

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient’s journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

At present, most spinal surgeons undertake pedicle screw implantation using either anatomical landmarks or C-arm fluoroscopy. Reported rates of screw malposition using these techniques vary considerably, though the evidence generally favors the use of image-guidance systems. A miniature spine-mounted robot has recently been developed to further improve the accuracy of pedicle screw placement. In this systematic review, we critically appraise the perceived benefits of robot-assisted pedicle screw placement compared to conventional fluoroscopy-guided technique. The Cochrane Central Register of Controlled Trials, PubMed, and EMBASE databases were searched between January 2006 and January 2013 to identify relevant publications that (1) featured placement of pedicle screws, (2) compared robot-assisted and fluoroscopy-guided surgery, (3) assessed outcome in terms of pedicle screw position, and (4) present sufficient data in each arm to enable meaningful comparison (>10 pedicle screws in each study group). A total of 246 articles were retrieved, of which 5 articles met inclusion criteria, collectively reporting placement of 1,308 pedicle screws (729 robot-assisted, 579 fluoroscopy-guided). The findings of these studies are mixed, with limited higher level of evidence data favoring fluoroscopy-guided procedures, and remaining comparative studies supporting robot-assisted pedicle screw placement. There is insufficient evidence to unequivocally recommend one surgical technique over the other. Given the high cost of robotic systems, and the high risk of spinal surgery, further high quality studies are required to address unresolved clinical equipoise in this field.