Pulse oximetry, although generally effective under ideal conditions, faces challenges in accurately estimating peripheral oxygen saturation (SpO) in complex clinical scenarios, particularly at lower saturation levels and in patients with darker skin ...
BACKGROUND: Consensus-based large language model (LLM) ensembles might provide an automated solution for extracting structured data from unstructured text in echocardiography reports.
BACKGROUND: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve identification is essential. The technology community typically favours objective metrics of pixel overlap on still-frame images, whereas clinical asses...
Machine learning (ML) algorithms hold significant potential for extracting valuable clinical information from big data, surpassing the processing capabilities of the human brain. However, it would be naïve to believe that ML algorithms can consistent...
BACKGROUND: Hypotension is associated with organ injury and death in surgical and critically ill patients. In clinical practice, treating hypotension remains challenging because it can be caused by various underlying haemodynamic alterations. We aime...
BACKGROUND: We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on per...
BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at greatest risk for postoperative complications. This trial examined the impact of machine learning models on clinician risk assessment.
Preoperative risk prediction is an important component of perioperative medicine. Machine learning is a powerful tool that could lead to increasingly complex risk prediction models with improved predictive performance. Careful consideration is requir...