Innovation in Abdominal Wall Reconstruction: From Advanced Robotic Techniques to Artificial Intelligence

Recent advances in the field of abdominal wall reconstruction (AWR) have been propelled by the integration of cutting-edge robotic surgery and digital innovations. Historically, AWR was limited to open or basic minimally invasive approaches, which often lacked precision, reproducibility, and consistent improvement in patient outcomes. With the introduction of robotics, the landscape of AWR has dramatically shifted, allowing for complex procedures such as retromuscular repairs and transversus abdominis release to be performed with greater accuracy and safety. Despite these advances, critical knowledge gaps remain regarding optimal patient selection, risk stratification, standardized definitions of hernia complexity, and the translation of technological progress into routine clinical practice. Furthermore, ongoing debates persist around the reproducibility of results, learning curves, and the generalizability of advanced robotic techniques, underscoring the need for evidence-based frameworks that support widespread adoption. Recent studies underscore the potential for artificial intelligence (AI) and machine learning (ML) to further enhance perioperative planning, outcome prediction, and decision-making, yet their real-world impact requires further validation. As the complexity and scope of AWR continue to evolve, a systematic, critical evaluation of these innovations is essential to define best practices and foster consistent improvement in care.This Research Topic aims to clarify how technological and methodological innovation can drive improvements in robotic abdominal wall reconstruction. Specifically, it seeks to identify how emerging surgical techniques, advanced imaging and navigation systems, and digital solutions such as AI and ML can enhance decision-making, optimize patient selection, and improve clinical outcomes. Through a critical appraisal of the literature and data-driven analyses, this topic will explore whether these novel tools and workflows deliver more reproducible and higher quality care. Furthermore, it aspires to define the future framework for standardized credentialing and training, ensuring the next generation of surgeons is equipped to leverage innovation responsibly and effectively.The Research Topic is limited to innovations directly related to robotic abdominal wall reconstruction, with a particular focus on the interface of technology, clinical practice, data-driven approaches, and education. Submissions are expected to address evidence-based improvements and strategic implementation of new tools or methodologies, rather than preliminary or preclinical research.To gather further insights in this area, we welcome articles addressing, but not limited to, the following themes:o Advanced robotic and minimally invasive techniques in AWR;o Integration of imaging modalities and navigation technologies;o Machine learning and predictive analytics for risk stratification and outcomes;o Standardization of hernia complexity and perioperative decision-making;o Training, credentialing, and the use of digital platforms in education;o Multicenter studies, registry-based analyses, and systematic reviews of clinical outcomes;o Patient-reported outcomes and quality improvement initiatives.Article types may include, but are not limited to, original research, systematic reviews, clinical trials, retrospective and prospective cohort studies, technical notes, and expert opinion pieces.Topic Editor Flavio Malcher declares financial relationships as a paid consultant for Medtronic, Intuitive, Allergan, Integra, Mesh Suture, Deep Blue, Origami, Becton Dickinson, and WL Gore. The other Topic Editor declares no competing interests with regard to the Research Topic subject. Topic editors: Flavio Malcher, Diego Laurentino Lima Source: https://www.frontiersin.org/research-topics/77362/innovation-in-abdominal-wall-reconstruction-from-advanced-robotic-techniques-to-artificial-intelligence