Latest AI and machine learning research in surgery for healthcare professionals.
BACKGROUND: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications ...
A Food and Drug Administration (FDA)-cleared artificial intelligence (AI) algorithm misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally diagnosed with an ischemic stroke. This scenario highlights a notable failure mode of AI tools, emphasizing the importance of human-machine interaction. In this report, the authors summarize the review processes by the FDA for softwa...
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and sur...
BACKGROUND: Pelvic lymph node dissection is a procedure performed in gastroenterological surgery, urology, and gynecology. However, due to discrepanci...
Purpose To compare the effectiveness of weak supervision (ie, with examination-level labels only) and strong supervision (ie, with image-level labels)...
Over the last few decades, shoulder surgery has undergone rapid advancements, with ongoing exploration and the development of innovative technologica...
Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reas...
To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenoca...
The most common route for drug administration is the oral route due to the various advantages offered by this route, such as ease of administration, c...
To establish a model based on clinical and delta-radiomic features within ultrasound images using XGBoost machine learning to predict proliferation-a...
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accur...
This chapter explores the transformative impact of deep learning (DL) on neurosurgery, elucidating its pivotal role in enhancing diagnostic performanc...
A large language model (LLM), in the context of natural language processing and artificial intelligence, refers to a sophisticated neural network that...
Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be c...
This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements ...
The advent of different realms of computational neurosurgery-including not only machine intelligence but also visualization techniques such as mixed r...
Over the past decade, advancements in computational modeling, augmented reality, and artificial intelligence (AI) have been driving innovations in spi...
Disorders affecting the neurological and musculoskeletal systems represent international health burdens. A significant impediment to progress with int...