Latest AI and machine learning research in surgery for healthcare professionals.
PURPOSE: Robotic-assisted surgery (RAS) generates vast amounts of video and robotic data, presenting opportunities for machine learning. Video-based models, in particular, that can temporally segment frames by ontological categories such as procedure type, phase, steps, actions, etc., are needed. Training separate models for each category neglects statistical dependencies between categories and ca...
OBJECTIVES: Large language models (LLMs) using a retrieval-augmented generation (RAG) approach have the ability to respond to user queries with answers grounded in specific sources. We conducted an exploratory evaluation of the accuracy of a RAG-based LLM to provide care recommendations for prehospital scenarios based on the emergency medical services (EMS) policies and treatment protocols (TPs). ...
Pancreatic ductal adenocarcinoma remains one of the deadliest malignancies, characterized by late diagnosis, aggressive biology and limited therapeuti...
This study employs Physics-Informed Neural Networks (PINNs) to simulate the thermal dynamics of biological tissue under laser irradiation by embedding...
Transient hypocalcemia is a common complication of total thyroidectomy. This study aimed to evaluate whether machine learning (ML)-based models could ...
BACKGROUND: Proximal junctional kyphosis (PJK) and failure (PJF) remain challenging and incompletely predictable adverse event following adult spinal ...
PURPOSE: The ergonomic challenges faced by surgeons during flexible ureteroscopy have yet to be thoroughly evaluated using objective methods. However,...
BACKGROUND: Lung resection is the gold-standard treatment for early stage lung cancer, but remains associated with significant mortality, highlighting...
BACKGROUND: The expansion of digitalization in the pre-, intra- and post-operative surgical phases allow the development and integration of advanced t...
BACKGROUND: Large language models (LLMs) are increasingly being explored in surgical training and clinical knowledge assessment. Although these models...
Infections after surgery remain a leading cause of morbidity and mortality, yet reliable risk stratification at the end of surgery is limited. Intraop...
BACKGROUND: The integration of intelligent technologies in operating room nursing represents a rapidly evolving field. Intelligent operating room nurs...
BACKGROUND: Informed consent (IC) documents in spine surgery frequently lack procedure-specific risk data, quantitative complication rates, and discus...
BackgroundIntraoperative consultation using frozen sections has been crucial for guiding surgical decisions, but has often been limited by the time an...
PURPOSE: To determine the prevalence of disability-free survival (DFS) five years after elective non-cardiac surgery in older adults, and to identify ...
A prospective observational cohort study. To determine whether machine learning models using radiomic features derived from preoperative MRI, clinical...
To address the unstable target localization and insufficient path planning efficiency caused by complex lighting and dynamic environments during autom...
INTRODUCTION: Currently, there are many diagnostic strategies for in-stent restenosis (ISR) used clinically, including invasive coronary angiography (...
Treatment outcome prediction plays an important role in realizing personalized cancer therapy. In triple-negative breast cancer (TNBC), neoadjuvant ch...
Artificial intelligence is rapidly expanding across medical fields, yet its integration into surgical practice remains limited. Understanding surgeons...