Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 7 appraisals
Clinical spine surgery
An Introduction to Machine Learning for the Practicing Spine Surgeon
The applications of new and emerging technologies in spine surgery are constantly expanding. Specifically, machine learning algorithms have seen a rise in utilization in clinical research, allowing for interpretation of large datasets that have the capability of experiential learning. The goal of this work is to present a guide for surgeons to better understand model design, key takeaways, and common pitfalls related to machine learning to ensure accurate and appropriate interpretation of analytical findings in their practice.
2 Aug 2026
Read appraisal →Journal of robotic surgery
Mapping the scientific landscape of robotic hernia repair: a bibliometric and topic modeling analysis of thematic transitions
Robotic hernia repair (RHR) has profoundly transformed modern surgery, yet it remains intensely debated. To understand this dynamic field, bibliometric analysis offers a powerful tool to evaluate its evolving research trends. Our study presents a comprehensive bibliometric and machine learning analysis of 1,503 publications from 2003 to 2025. The literature demonstrates a growth trend (R2 = 0.96), characterized by initial latency (2003-2015), steady adoption (2016-2020) and a recent exponential explosion (2020-2025) peaking at 262 publications in 2025. The United States is the leading contributing nation (n = 892), followed by Italy (n = 98), Germany (n = 71), and China (n = 68). Key contributors include Kudsi OY, Malcher F, and Muysoms F, with Surgical Endoscopy serving as the predominant publication venue (n = 203). Spatial and relational networks were mapped using VOSviewer and CiteSpace. Critically, Latent Dirichlet Allocation (LDA) topic modeling combined with principal component analysis (PCA) stratified the corpus into four major research domains comprising the anatomical spectrum, the balance between resource and risk management, hardware evolution and surgical education, and technical complexity coupled with high-level evidence. Longitudinal regression of thematic probabilities revealed a significant thematic transition in research emphasis from early hardware feasibility and technical exploration toward value-based evaluation. Current research priorities are increasingly focused on rigorous cost-effectiveness evaluation in high-risk populations together with the generation of high-quality randomized evidence. In conclusion, this multidimensional analysis illustrates how this field is increasingly guided by the interconnected domains of Evidence, Education and Economics, which align with established frameworks for sustainable surgical progress.
21 June 2026
Read appraisal →Scientific data
Appendix300: Surgical video and patient metadata of 330 laparoscopic appendectomy cases from five institutions
The limited availability of diverse and representative training data poses a critical barrier to the development of clinically relevant computational tools for intraoperative surgical decision support. Surgical procedures are not routinely recorded, and data annotation requires domain expertise, resulting in a scarcity of open-access surgical video datasets with high-quality annotations. Existing datasets are typically limited to single institutions and specific procedures, such as cholecystectomy, and rarely comprise patient-level metadata like demographic characteristics, disease history, or laboratory parameters. The Appendix300 dataset comprises 330 laparoscopic surgery recordings, including 325 full-length laparoscopic appendectomies and 5 control recordings from non-appendectomy procedures in pediatric and adult patients treated at five German centers. The dataset includes patient-level clinical metadata (demographics, medical history, clinical symptoms, preoperative laboratory parameters, and histopathologic findings, as well as standardized expert annotations of the laparoscopic grade of appendicitis). This dataset enables novel validation tasks for computer vision in laparoscopic surgery and facilitates simulation of decentralized learning approaches, overall enhancing the breadth and translational relevance of AI-based surgical video analysis.
14 June 2026
Read appraisal →Journal of the American College of Surgeons
Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified Delphi Consensus
BACKGROUND: Artificial intelligence (AI)-enabled clinical decision support systems (CDSS) demonstrate performance comparable or superior to human experts in certain tasks. However, their integration into surgical practice faces a significant implementation gap, alongside ethical, privacy, and legal concerns. Clear governance frameworks are needed to guide their responsible adoption in surgery, to prevent inconsistent application, care quality variation, and exacerbation of algorithmic bias. Here, we establish a systematic, evidence-based, and consensus-driven framework to guide the ethical, effective, and sustainable adoption of AI-enabled CDSS in surgery. STUDY DESIGN: A systematic literature review was conducted of PubMed, Cochrane Library, Medline, and Embase databases until 2024 to identify key governance themes. The themes informed the generation of candidate items, which were then refined through a multiround expert panel consensus process using a modified Delphi approach to produce the final framework. RESULTS: Thematic analysis of 80 full-text articles meeting inclusion criteria identified 4 overarching themes for AI governance: (1) technical prerequisites and model design; (2) clinical implementation and human factors; (3) ethics, safety, and trustworthiness; and (4) bias, fairness, and equity. Panel consensus evaluation resulted in the development of a 19-item framework. CONCLUSIONS: The consensus-driven framework presented here provides foundational guidance essential for navigating the complexities of implementing AI-enabled CDSS safely and ethically in surgery. Addressing the considerations outlined across these 4 core themes can facilitate the responsible adoption of AI, accelerating the transition toward an advanced, data-driven surgical practice while mitigating potential risks.
13 June 2026
Read appraisal →Journal of robotic surgery
Global evolution of robot-assisted cholecystectomy research in the era of artificial intelligence: a bibliometric and knowledge-mapping study
OBJECTIVE: To systematically map the global evolution, collaborative networks, knowledge structure, and emerging research hotspots in robot-assisted cholecystectomy (RAC) using bibliometric and visualization techniques. METHODS: A comprehensive bibliometric analysis was conducted using the Web of Science Core Collection (2005-2025). Publications were retrieved using predefined search strategies and screened according to strict inclusion criteria. CiteSpace, VOSviewer, and R software were employed to analyze publication trends, co-authorship networks, institutional and national collaborations, co-citation patterns, keyword co-occurrence, and research bursts. Knowledge mapping techniques were used to visualize thematic evolution and intellectual structure. RESULTS: A total of 926 eligible publications were included. Global output demonstrated a continuous and marked increase, particularly after 2016, with citations following a similar upward trajectory, reflecting growing academic impact. The United States dominated both publication output and citation influence, while collaboration networks remained largely regionally clustered with limited cross-national integration. Research was primarily concentrated in high-impact surgical journals, with foundational contributions emphasizing feasibility, safety, and comparative effectiveness. Keyword and co-citation analyses revealed a knowledge structure centered on laparoscopic surgery, bile duct injury, and robotic systems, with emerging clusters highlighting artificial intelligence (AI) and surgical education. Evolutionary trajectory analysis demonstrated a transition from technical feasibility (early stage), to safety and evidence-based evaluation (middle stage), and to training and technological integration (recent stage). Burst detection further identified recent hotspots in AI, cost-effectiveness, and surgical training systems. Emerging evidence also indicates increasing integration of AI-driven systems into RAC, enabling intraoperative decision support, workflow recognition, and early-stage semi-autonomous surgical execution, particularly in standardized procedures such as cholecystectomy. CONCLUSION: RAC research has evolved from early exploratory studies toward increasing technological integration and methodological refinement. Although RAC is not currently among the most dominant clinical indications for robotic surgery, it provides a valuable model for studying surgical standardization, training systems, and emerging intelligent surgical technologies. While current applications remain largely assistive, emerging advances in AI and robotic technologies suggest the potential for future development toward more intelligent and partially automated surgical systems. However, most evidence currently remains experimental and has not yet translated into widespread clinical practice. Future research should focus on strengthening global collaboration, improving high-quality evidence generation, and carefully evaluating the safety, feasibility, and clinical applicability of emerging intelligent surgical technologies.
26 May 2026
Read appraisal →Journal of robotic surgery
Robotic versus laparoscopic intracorporeal anastomosis learning curve: a systematic review
The learning curve for robotic colorectal surgery is increasingly characterised. However, the specific learning curve for intracorporeal anastomosis (IA) remains poorly defined. This review compares robotic and laparoscopic IA with respect to operative time, cases required for proficiency, complication rates, and conversion to extracorporeal anastomosis (EA). A systematic review was conducted in accordance with PRISMA guidelines. The protocol was registered on Open science framework ( https://doi.org/10.17605/OSF.IO/K4MYZ ). On March 18, 2025, OVID and PubMed were searched using the search terms; "laparoscopic," "robotic," "anastomosis," "learning," "education," and "training." Yielding a total of 273 results from PubMed (179) and OVID (94). After screening a total of thirteen studies were included. Thirteen studies met inclusion criteria, including eight comparative analyses and six directly comparing robotic and laparoscopic techniques. Four studies evaluated robotic learning curves without a comparator group, while one was a literature review incorporating institutional experience. Most studies were retrospective (n = 8), with two prospective clinical studies and two experimental simulation-based studies. Hybrid approaches (robotic mobilisation with laparoscopic EA) contributed to heterogeneity (Reitz et al. 2018, https://doi.org/10.1007/s00464-018-6074-7 ). Learning curve assessment was heterogeneous, with most studies using cumulative sum (CUSUM) or risk-adjusted CUSUM (RA-CUSUM) analyses based on operative time or technical performance metrics. Proficiency in robotic IA was reported after approximately 11-29 cases in some series and around 20 cases in others, although larger studies of robotic colorectal surgery demonstrated broader ranges of up to 100 cases. Robotic right hemicolectomy with IA demonstrated a shorter learning curve and reduced anastomosis time compared with laparoscopic IA. Complication rates, conversion rates, and oncological outcomes were comparable between approaches (Gachabayov et al. 2019, Surg Technol Int, 34:163-168). Robotic intracorporeal anastomosis, particularly in right hemicolectomy, appears to have a shorter and less technically demanding learning curve compared with laparoscopic techniques, without compromising safety or oncological outcomes. These findings support a potential role for robotic platforms in facilitating adoption of intracorporeal anastomosis. However, current evidence is limited by retrospective design, small cohorts, and inconsistent definitions of proficiency (Gachabayov et al. 2019, Surg Technol Int, 34:163-168; Van Eetvelde et al. 2022, https://doi.org/10.1007/s11701-022-01514-6 ).
14 May 2026
Read appraisal →Journal of surgical education
Non-Invasive Assessment of Continuous Real-Time Workplace Stress Among Surgeons and Surgical Trainees: A Scoping Review
OBJECTIVES: To describe how physiological parameters were used to characterize continuous, real-time workplace stress among surgeons and surgical trainees, and to identify the advantages and disadvantages of using wearable devices. DESIGN: We conducted a scoping review to examine how wearable devices were used to objectively measure surgeons' and trainees' physiological stress in the workplace. Peer-reviewed English articles published up to September 2023 in Medline (Ovid), Cochrane Library (Wiley), Embase (Elsevier), and PsycINFO (Ebsco) databases using wearable devices used to measure continuous, real-time physiological parameters representing workplace stress among surgeons and surgical trainees were included in the review. SETTING: We included studies that assessed physiological stress in the workplace (e.g., operating room and other clinical environments). PARTICIPANTS: Surgeons and surgical trainees. RESULTS: A review of 61 articles that met inclusion criteria revealed that the most commonly used wearable devices were Polar 13 (21.3%), Medilog: 6 (9.8%), Holter ECG device: 7 (11.5%), and WHOOP! : 6 (9.8%). Physiological parameters used to measure continuous, real-time workplace stress were heart rate, heart rate variability, blood pressure, respiratory rate, and electrodermal activity. Physiological parameters indicated that surgeons were more stressed in the operating room than in other clinical environments. Intraoperative surgeons' stress increased with performing critical steps of the operation, teaching a trainee, the number of intraoperative events, e.g., bleeding, and decreased with years of experience. There was a negative relationship between stress and the surgeon's intraoperative performance. The advantages of using wearable devices' physiologic parameters were objectivity, longitudinal measurement, and minimally obtrusive. The disadvantages were short battery life, inadequate data capture, artifacts, and data loss. CONCLUSIONS: Wearable devices can measure a range of physiological parameters that represent workplace stress continuously among surgeons and surgical trainees.
5 May 2026
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