The improved stability and controllability of robotics has the potential to expand the spectrum of reconstructive surgery. Until recently, however, no robot was designed specifically for microsurgery. The present study aims to summarize the clinical pearls and pitfalls of utilizing the Symani Surgical System in microsurgical practice. The PubMed Database was queried using the search term “Symani Robot System” from inception until January 2025. Data regarding demographics, study design, technique, and surgical outcomes were extracted from the full text by two independent reviewers. In the present analysis, 21 publications encompassing the results of 397 patients (range: 1–100) and 642 robotic anastomoses were included. Of the 335 patients included in the studies that reported complication rates, pooled analysis yielded an overall complication rate of 14.03% (n = 47) and a partial or complete flap loss rate of 2.09% (n = 7). Of 642 robotic total anastomoses, only 1.24% (n = 8) required a transition to a manual approach. A total of 19 groups (90.47%) reported high precision of Symani, 15 (71.43%) highlighted improved surgical access to deeper anatomical fields, and 12 (57.15%) reported enhanced microsurgical ergonomics. Regarding limitations, 13 (61.90%) cited the expense of the system, 5 (23.81%) reported a lack of haptic feedback, 5 (23.81%) identified instrument “stickiness” as a factor slowing operations, and 3 (14.29%) highlighted the need for improved grip. The learning curve associated with Symani was discussed in 15 studies (71.43%). The results of the present literature review demonstrate that the Symani robot offers numerous benefits that will help advance the field of microsurgery. It provides complete tremor elimination, motion scaling, and improved operative ergonomics, leading to patent anastomosis and complication rates comparable to a traditional manual approach. However, more data are needed before widespread clinical implementation to determine whether the increased precision and controllability outweigh the cost and learning curve. The present study highlights the use of the Symani Surgical System, a novel robotic system designed specifically for microsurgery. Symani offers increased precision, dexterity, controllability, and numerous benefits that will help advance the field.

Background: Artificial intelligence (AI) and machine learning models are frequently developed in medical research to optimize patient care, yet they remain rarely used in clinical practice. Objective: This study aims to understand the disconnect between model development and implementation by surveying physicians of all specialties across the United States. Methods: The present survey was distributed to residency coordinators at Accreditation Council for Graduate Medical Education–accredited residency programs to disseminate among attending physicians and resident physicians affiliated with their academic institution. Respondents were asked to identify and quantify the extent of their training and specialization, as well as the type and location of their practice. Physicians were then asked follow-up questions regarding AI in their practice, including whether its use is permitted, whether they would use it if made available, primary reasons for using or not using AI, elements that would encourage its use, and ethical concerns. Results: Of the 941 physicians who responded to the survey, 384 (40.8%) were attending physicians and 557 (59.2%) were resident physicians. The majority of the physicians (651/795, 81.9%) indicated that they would adopt AI in clinical practice if given the opportunity. The most cited intended uses for AI were risk stratification, image analysis or segmentation, and disease prognosis. The most common reservations were concerns about clinical errors made by AI and the potential to replicate human biases. Conclusions: To date, this study comprises the largest and most diverse dataset of physician perspectives on AI. Our results emphasize that most academic physicians in the United States are open to adopting AI in their clinical practice. However, for AI to become clinically relevant, developers and physicians must work synergistically to design models that are accurate, accessible, and intuitive while thoroughly addressing ethical concerns associated with the implementation of AI in medicine.

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