AI Medical Compendium Journal:
BMC anesthesiology

Showing 1 to 10 of 24 articles

Bridging the gap between scientists and clinicians: addressing collaboration challenges in clinical AI integration.

BMC anesthesiology
This article explores challenges for bridging the gap between scientists and healthcare professionals in artifical intelligence (AI) integration. It highlights barriers, the role of interdisciplinary research centers, and the importance of diversity,...

Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.

BMC anesthesiology
BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain manag...

Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.

BMC anesthesiology
BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Cu...

Comparison of AI applications and anesthesiologist's anesthesia method choices.

BMC anesthesiology
BACKGROUND: In medicine, Artificial intelligence has begun to be utilized in nearly every domain, from medical devices to the interpretation of imaging studies. There is still a need for more experience and more studies related to the comprehensive u...

An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.

BMC anesthesiology
AIM: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.

Unravelling intubation challenges: a machine learning approach incorporating multiple predictive parameters.

BMC anesthesiology
BACKGROUND: To protect patients during anesthesia, difficult airway management is a serious issue that needs to be carefully planned for and carried out. Machine learning prediction tools have recently become increasingly common in medicine, frequent...

The anesthesiologist's guide to critically assessing machine learning research: a narrative review.

BMC anesthesiology
Artificial Intelligence (AI), especially Machine Learning (ML), has developed systems capable of performing tasks that require human intelligence. In anesthesiology and other medical fields, AI applications can improve the precision and efficiency of...

Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.

BMC anesthesiology
BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient ...

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis.

BMC anesthesiology
BACKGROUND: Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to ...

The effects of robot-assisted laparoscopic surgery with Trendelenburg position on short-term postoperative respiratory diaphragmatic function.

BMC anesthesiology
OBJECTIVE: To study how Pneumoperitoneum under Trendelenburg position for robot-assisted laparoscopic surgery impact the perioperative respiratory parameters, diagrammatic function, etc. METHODS: Patients undergoing robot-assisted laparoscopic surger...