Aim: Insufficient assessment of post-surgical organ perfusion in hepatobiliopancreatic surgery can lead to serious complications. Consequently, various technological solutions have been developed to achieve non-invasive and accurate blood flow assessment. This article aims to evaluate the current state of four-dimensional flow magnetic resonance imaging (4D-flow MRI) and computational fluid dynamics (CFD) technologies in assessing vascular blood flow within this surgical field. Methods: A comprehensive literature search using ClinicalTrials.gov and PubMed/MEDLINE was performed; articles published between 2015 and 2025 were included. Broad search terms, including “blood flow measurement”, “4D-flow MRI”, or “computational fluid dynamics” and “abdomen” or “liver”, were utilized. Results: Twenty-two studies were analyzed in detail. Nineteen focused on vascular conditions surrounding the liver, with 15 assessing venous flow and five evaluating the hepatic artery. Additional hemodynamic features analyzed included blood velocity, pressure, and particle distribution. The clinical applications investigated were: portal vein embolization (1), venous anastomosis (3), liver resection (2), portal hypertension (2), transarterial radioembolization (2), transjugular intrahepatic portosystemic shunt (4), and liver fibrosis (1). Notably, only CFD facilitated the simulation of prospective hemodynamic conditions (2). Conclusion: Both 4D-flow MRI and CFD technologies facilitate the accurate study of blood flow dynamics within the supramesocolic compartment. Furthermore, CFD enables the simulation of prospective vascular conditions, establishing its potential as a preoperative planning tool. However, further research is required to fully validate the clinical utility of CFD in this surgical context.

Aim: Nerve dysfunction often manifests as abnormal eye behaviors, necessitating accurate and objective neurological assessment. Current deep learning-based facial analysis methods lack adaptability to inter-patient variability, making it difficult to capture subtle and rapid ocular dynamics such as incomplete eyelid closure or asymmetric eye movement. To address this, we propose a precise deep learning system for quantitative ocular state analysis, providing objective support for the evaluation of neurological dysfunction. Methods: We propose the Ocular-enhanced Face Keypoints Net (OFKNet). It incorporates three key innovations: (1) a 40-point anatomically informed ocular landmark design enabling dense modeling of eyelid contours, canthus structure, and pupil dynamics; (2) a MobileNetV3-based region enhancement module that amplifies feature responses within clinically critical areas such as the internal canthus; (3) and an improved Path Aggregation Network combined with Squeeze-and-Excitation modules that enables adaptive multi-scale fusion and enhances sensitivity to subtle ocular deformations. Results: Using clinically acquired data, OFKNet demonstrates substantial performance gains over state-of-the-art baselines. It achieves a 65.3% reduction in normalized mean error on the 40-point dataset (0.029 vs. 0.084) and a 38.4% reduction on the 14-point dataset, with all improvements statistically significant (P < 0.001). Despite operating on high-resolution inputs, the system maintains real-time capability and provides stable frame-level landmark localization, enabling precise capture of dynamic ocular motion patterns. Conclusion: OFKNet provides a reliable tool for real-time monitoring of eye movement patterns in patients with neurological disorders. By visualizing time-series graphs of bilateral eye openness, the system enables a more comprehensive understanding of ocular dynamics and supports timely clinical decision-making and treatment adjustment.

Gastroesophageal varices (GEVs) are one of the first clinically relevant consequences of PH, developing in 60%-80% of patients with liver cirrhosis. They are directly associated with a higher risk of decompensation and death. Screening endoscopy is the most common screening strategy in patients with cirrhosis. However, there is a tendency to find non-invasive predictors of GEVs to avoid costly and potentially harmful procedures safely. Artificial intelligence (AI)-driven predictive models effectively integrate diverse clinical, imaging, and laboratory data to provide non-invasive and precise risk stratification, reducing the reliance on endoscopic evaluations. Deep learning applications, particularly convolutional neural networks (CNNs), have proved highly effective in analyzing endoscopic images, thereby enhancing diagnostic accuracy beyond traditional visual inspection. Additionally, radiomics-based AI models utilizing computed tomography (CT) and elastography have enabled non-invasive risk assessment, improving predictions of bleeding risk and estimations of the hepatic venous pressure gradient (HVPG). Ethical considerations, such as data privacy and algorithmic bias, also require careful management. Future research should focus on prospective validation, real-world application studies, and the development of standardized AI frameworks to ensure the clinical applicability of these methods. AI-driven precision medicine has the potential to revolutionize the management of GEVs, offering more efficient, accurate, and individualized patient care while optimizing healthcare resource utilization.

Aim : The use of chatbots to respond across various domains is becoming more integrated into daily life, potentially replacing traditional search engines. The study aimed to investigate the performance of different large language models (LLMs) in providing recommendations regarding pancreatic cancer (PC) to surgeons. Methods : Standardized prompts were engineered to query four freely accessible LLMs (ChatGPT-4, Personal Intelligence by Inflection AI, Anthropic Claude 3 Haiku Version 3.5, Perplexity AI) on October 9th, 2024. Fourteen questions included the incidence, diagnosis, and treatment for radiologically resectable, borderline resectable, locally advanced, and metastatic PC. Three different investigators queried the LLMS simultaneously. The reliability and accuracy of the responses were evaluated using a 4-point Likert scale and then compared to the international guidelines. Descriptive statistics were used to report outcomes as counts and percentages. Results : Overall, 72% of the responses were deemed correct (scored 3 or 4). Claude provided the most accurate responses (32%), followed by ChatGPT (28%). ChatGPT-4 and Anthropic Claude 3 Haiku Version 3.5 achieved the overall highest score rate (4-point) at 50% and 52%, respectively. Regarding the quality and accuracy of the responses, ChatGPT cited guidelines most frequently (29%). However, only 19% of all evaluated responses included guideline citations. Conclusion : The LLMs are still not suitable for safe, standalone use in the medical field, but their rapid learning capabilities suggest they may become indispensable tools for medical professionals in the future.

Artificial intelligence (AI) is starting to change the way we approach hernia surgery and abdominal wall reconstruction (AWR). From improving surgical planning to predicting outcomes and enhancing intraoperative precision, AI, particularly deep learning models (DLMs), offers tools that can support decision making and personalize patient care. These models can analyze large datasets, such as preoperative imaging, to spot patterns we might miss. We have seen AI outperform experienced surgeons in predicting complications like mesh infections. Despite its promise, there are also valid concerns about data quality, transparency, and overreliance. Thoughtful integration is essential, ensuring that AI complements rather than replaces clinical judgment. Moving forward, collaboration with fields such as computer science and support from surgical societies will be essential. With appropriate groundwork, AI can be a powerful tool in the pre-op, intra-op, and post-op phases of care.

Artificial intelligence (AI) is reshaping healthcare, particularly within the realm of spinal surgery, enhancing diagnostics, treatment, and patient management. AI is not only enhancing the technical aspects of spinal surgery but also revolutionizing patient care through personalized management, setting a new standard within the field. This computational renaissance has received increasing attention from providers and regulatory bodies to ensure novel technologies are being safely and effectively used. This review explores contemporary uses of AI in adult spinal deformity (ASD) surgery and the extent of their validation. Given the increasing complexity of ASD surgery and the expanding capabilities of AI, this review is essential to synthesize current applications, evaluate methodological strengths and limitations, and highlight future research opportunities in this evolving field.

From novel reconstructive operations to the creation of negative pressure wound therapy, plastic surgery is defined by a rich history of surgical and technological innovation. One of the latest technologies changing the face of medicine is artificial intelligence (AI), with its increasing popularity embodied by the meteoric rise in AI-related publications over the last decade. Abdominal wall reconstruction (AWR) is a discipline within plastic surgery that has taken an interest in AI technology, incorporating it into research to better understand hernia outcomes, interpret preoperative data, and improve patient-specific care and education. This review aims to explore the current breadth of AI use within AWR to give readers a better understanding of where the field currently stands and inspire ideas for where it may go in the future.

Aim: Automated surgical skill assessment is poised to become an invaluable asset in surgical residency training. In our study, we aimed to create deep learning (DL) computer vision artificial intelligence (AI) models capable of automatically assessing trainee performance and determining proficiency on robotic suturing tasks. Methods: Participants performed two robotic suturing tasks on a bench-top model created by our lab. Videos were recorded of each surgeon performing a backhand suturing task and a railroad suturing task at 30 frames per second (FPS) and downsampled to 15 FPS for the study. Each video was segmented into four sub-stitch phases: needle positioning, targeting, driving, and withdrawal. Each sub-stitch was annotated with a binary technical score (ideal or non-ideal), reflecting the operator’s skill while performing the suturing action. For DL analysis, 16-frame overlapping clips were sampled from the videos with a stride of 1. To extract the features useful for classification, two pretrained Video Swin Transformer models were fine-tuned using these clips: one to classify the sub-stitch phase and another to predict the technical score. The model outputs were then combined and used to train a Random Forest Classifier to predict the surgeon's proficiency level. Results: A total of 102 videos from 27 surgeons were evaluated using 3-fold cross-validation, 51 videos for the backhand suturing task and 51 videos for the railroad suturing task. Performance was assessed on sub-stitch classification accuracy, technical score accuracy, and surgeon proficiency prediction. The clip-based Video Swin Transformer models achieved an average classification accuracy of 70.23% for sub-stitch classification and 68.4% for technical score prediction on the test folds. Combining the model outputs, the Random Forest Classifier achieved an average accuracy of 66.7% in predicting surgeon proficiency. Conclusion: This study shows the feasibility of creating a DL-based automatic assessment tool for robotic-assisted surgery. Using machine learning models, we predicted the proficiency level of a surgeon with 66.7% accuracy. Our dry lab model proposes a standardized training and assessment tool for suturing tasks using computer vision.

Healthcare applications of artificial intelligence (AI) and machine learning (ML) are currently in a stage of exponential growth; however, their adoption into clinical practice across clinical specialties remains uneven. In spine surgery, the presence of challenging clinical problems, advanced intraoperative technologies, and large multi-center datasets positions the field well for the integration of these technologies into the clinic and operating room. Here, we review recent advances in AI/ML applications in several key domains of spine surgery, identify methodological challenges shared by many approaches, and suggest solutions that may lead to these approaches becoming validated, commercialized tools that can reach clinical practice. Ultimately, we aim for this narrative review to help catalyze further progress in the development and commercialization of AI/ML to benefit future spine patients.

The distribution pattern of esophageal cancer lymph node metastases and consequently the lymph node stations that should be resected are currently being investigated in the international distribution of lymph node metastases in esophageal carcinoma (TIGER) study. This observational study has no specific entry criteria, resulting in significant variation among participating centers regarding the extent of lymphatic tissue clearance during surgery. To ensure reliable interpretation of the TIGER study results, this study aims to develop an intraoperative metric (surgical quality assessment tool) to examine the extent of lymphadenectomy. This multicenter prospective study assesses the extent of lymphadenectomy during esophagectomies performed within the TIGER study. After consensus on the applicability of surgical quality assessment in observational studies (phase 1), a short photo/video of the thoracic, abdominal, and [if applicable] cervical area will be captured after lymphadenectomy (phase 2). Those images will be rated by expert surgeons using a structured assessment tool (phase 3), and an automatic artificial intelligence (AI)-based quality assessment will be developed (phase 4). The variability in lymphadenectomy will be adjusted when analyzing TIGER study outcomes. Technique standardization by surgical quality assurance (SQA) reduces variation in trial outcomes. This cannot be applied to the TIGER study because of its observational nature, although surgical heterogeneity might influence TIGER study outcomes. To monitor surgical performance within observational studies, surgical quality assessment can be applied, although this has not been done yet. This study will ensure a reliable interpretation of TIGER study results and could be used as a benchmark for quality assessment in future surgical studies.