Surgery

Artificial Intelligence in Surgery Revisited: A 2025 Update on Machine Learning for Predicting Complications and Outcomes.

Machine learning (ML), a branch of artificial intelligence, is rapidly transforming surgical complication and outcome prediction. Unlike traditional statistical approaches, ML can learn complex, nonlinear relationships across multiple variables, enabling more accurate and adaptable prognostication. Emerging ML-based tools have demonstrated strong performance across diverse surgical specialties, of...

The Investigation of an Agile Mobile Quadruped Robot in Forensic Death Scenes.

The Fourth Industrial Revolution has accelerated the integration of robotics and artificial intelligence (AI) into workplaces, creating opportunities to merge technology with real-world practice. In forensic pathology, workforce shortages and the dangers of investigating hazardous death scenes highlight the need for innovation. This study explored the use of a mobile, agile quadruped robot to inve...

Perioperative Extracorporeal Membrane Oxygenation Use in Lung Transplantation: Analysis of Outcomes and Future Directions.

Extracorporeal membrane oxygenation (ECMO) has emerged as a critical intervention in the management of patients with end-stage lung disease undergoing...

Machine learning-based prediction of postpartum hemorrhage using maternal clinical and biochemical features.

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hys...

Accuracy of Robotic Root Canal Localization Using Optimized Point Cloud Registration Strategies: An In Vitro Study.

INTRODUCTION: Traditional registration process of robotic root canal localization remains cumbersome. This study aimed to determine optimal point clou...

Machine learning-based prediction of short-term recurrence of colorectal adenomatous polyps following EMR: model development and validation study.

OBJECTIVE: This study aims to develop and validate a model for predicting the 1-year recurrence of adenomatous polyps following endoscopic mucosal res...

Body composition radiomics combined with machine learning for early recurrence prediction in intrahepatic cholangiocarcinoma following curative surgery: A Multi-Center study.

PURPOSE: Early recurrence (ER) of intrahepatic cholangiocarcinoma (ICC) after curative hepatectomy correlates with dismal prognosis. We hypothesized t...

Robotic thoracic surgery: what is different in anesthesia?

PURPOSE OF REVIEW: Robotic-assisted thoracic surgery (RATS) has emerged as a transformative approach in thoracic surgery, enabling enhanced precision ...

Development of a Machine Learning-Based Predictive Model for Anastomotic Leakage Following Gastric Cancer Surgery.

BackgroundThe incidence of anastomotic leakage (AL) following radical gastrectomy for gastric cancer ranges from 2.1% to 14.6%, with mortality rates u...

Artificial Intelligence in Surgery Revisited: Leveraging AI for Postoperative Instructions, Clinical Support, and Intelligent Monitoring.

Artificial intelligence (AI) is rapidly transforming surgical care, with growing integration across all phases from preoperative planning to postopera...

From growing pains to growing evidence: a 2025 update on novel insights in lower limb osteochondroses.

PURPOSE OF REVIEW: Osteochondrosis encompasses a heterogenous group of pathologies affecting endochondral ossification in the growing child and adoles...

Augmenting decision making in acute care surgery: A systematic review of machine learning-driven risk prediction models.

BACKGROUND: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning ...

Artificial-Intelligence-based Surgical Phase Recognition in Robot-Assisted Radical Prostatectomy and Cross-Surgeon Validation.

BACKGROUND: Artificial intelligence (AI) has shown potential in various fields; however, its practical application in surgery remains limited. We deve...

Performance of machine learning algorithms in predicting the need for surgical fixation in pediatric craniomaxillofacial trauma.

Machine learning offers a novel approach to improve surgical triage in pediatric craniomaxillofacial trauma, where decision-making often relies on cli...

Patients' and physiotherapists' experiences with robotic technologies for lower extremity rehabilitation following spinal cord injury: a reflexive thematic analysis.

PURPOSE: To explore perceived benefits, barriers and motivational factors related to exercising with the robotic device ROBERT® among patients undergo...

Perioperative Acute Kidney Injury: What's New in 2025?

Perioperative acute kidney injury (AKI) is a frequent complication that affects outcomes well beyond the perioperative period. Its mechanisms remain i...

A systematic review of the role of wearable devices and artificial intelligence applications in assessing functional outcomes after lumbar fusion.

BACKGROUND CONTEXT: As the population ages, rates of lumbar spine disease have risen, and lumbar fusion surgeries have become more prevalent. There ha...

The Pediatric Surgeon's AI Toolbox: How Large Language Models Like ChatGPT Are Simplifying Practice and Expanding Global Access.

INTRODUCTION: Pediatric surgeons face substantial administrative workload. Large language models (LLMs) may streamline documentation, family communica...

AI in Robotic-assisted Pediatric Surgery: Current Applications and Future Directions.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into surgical practice, offering enhanced decision-making, precision, and workflow...

A data-driven modeling approach to prediction of persistent foot drop after gastroc-soleus lengthening surgery in children with cerebral palsy.

BACKGROUND: Gastrocsoleus lengthening (GSL) is an established surgical intervention for equinus deformity in children with cerebral palsy (CP). While ...