The aim of this article is to investigate the effect of the coronavirus disease 2019 (COVID-19) pandemic on our surgical department, which is situated in Athens, Greece, as well as to review published literature on the COVID-19 pandemic's impact on surgical activities in our department. We retrospectively reviewed the surgical procedures that were performed in the surgical department of a tertiary University hospital in Athens, Greece, before and during the pandemic. Furthermore, we performed a literature review evaluating articles on surgical activity and COVID-19 published from the beginning of the pandemic up until the January of 2022 on the PubMed database. In total, 894 patients were included in the study. Of those, 264 (29.5%) underwent surgery during the control period and 630 (70.5%) in the pandemic period. Overall, we performed 20.5% fewer surgeries in the post-sanitary period. In particular, elective surgeries decreased on average by 23.9%, emergency procedures decreased by 8.9%, and oncology surgeries increased by an average of 6.4% after the year 2020. Concerning the review of literature, 51 studies were selected for this review. According to them, the main effect of the pandemic on the surgical sector was reflected in the reduction of total surgeries, mainly due to the postponement of elective surgical procedures, which showed a median reduction of 54% compared to the pre-COVID-19 period. A smaller decrease was observed in the number of emergency and oncological surgeries. Reduced surgical activity during the pandemic, due to the health measures imposed, requires courageous corrective interventions to avoid its adverse effects, such as disease progression, increased treatment costs, reduced quality of life, and ultimately the survival of the patients.

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to support surgical decision-making in complex HPB cases. Methods: This narrative review synthesized the existing literature on the applications, benefits, limitations, and implementation challenges of 3D reconstruction and AI technologies in HPB surgery. Results: The literature suggests that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons’ understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms can automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusions: The convergence of 3D visualization and AI-driven analytics is contributing to an emerging paradigm shift in HPB surgery. This combination may foster a more personalized, precise, and data-informed surgical approach, particularly in anatomically complex or high-risk cases. However, current evidence is heterogeneous and largely observational, underscoring the need for prospective multicenter validation before routine implementation.

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to enhance surgical management in complex HPB cases. Methods: This narrative review comprehensively synthesized the existing literature on the applications, benefits, and limitations of 3D reconstruction and AI technologies in the context of HPB surgery. Results: The literature demonstrates that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons' understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusion: The convergence of 3D visualization and AI-driven analytics is creating a paradigm shift in HPB surgery. This powerful combination is fostering a move toward a more personalized, precise, and data-informed surgical approach, with the potential to optimize outcomes for the most challenging patient cohorts.