AIMC Topic: Anesthesia

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Artificial intelligence in anesthesia: comparison of the utility of ChatGPT v/s google gemini large language models in pre-anesthetic education: content, readability and sentiment analysis.

BMC anesthesiology
BACKGROUND: Large Language Models (LLMs) such as ChatGPT and Google Gemini are increasingly explored for their potential in patient education, particularly in the perioperative setting. As text-based tools trained on extensive datasets, they can gene...

Artificial Intelligence in Pediatric Anesthesia.

Anesthesiology clinics
This text explores the integration of artificial intelligence (AI) into pediatric anesthesiology, highlighting its potential to enhance safety, efficiency, and decision-making throughout the perioperative period. It addresses the unique challenges of...

The Role of Artificial Intelligence in Anesthesia Monitoring and Surveillance.

Anesthesiology clinics
Artificial intelligence (AI) has the potential to significantly improve monitoring in the operating room, allowing us to detect and predict changes in the patient's physiology sooner and better optimize patient care. Currently, clinically available a...

Advancements in Neuroanesthesia Through Artificial Intelligence.

Anesthesiology clinics
Artificial intelligence (AI) is transforming neuroanesthesia by enhancing precision and efficiency in managing patients during neurosurgical procedures. AI uses advanced algorithms and machine learning techniques to predict complications, optimize an...

Artificial Intelligence in Cardiovascular and Thoracic Anesthesia.

Anesthesiology clinics
Recent breakthroughs in artificial intelligence (AI) have particularly shone in cardiothoracic anesthesia, where its ability to efficiently analyze complex datasets and process vast amounts of information in mere moments has captured considerable att...

Leveraging advanced graph neural networks for the enhanced classification of post anesthesia states to aid surgical procedures.

PloS one
Anesthesia plays a pivotal role in modern surgery by facilitating controlled states of unconsciousness. Precise control is crucial for safe and pain-free surgeries. Monitoring anesthesia depth accurately is essential to guide anesthesiologists, optim...

Enhancing Ophthalmic Anesthesia Optimization with Predictive Embedding Models.

SLAS technology
Ophthalmic anesthesia the crucial factors in success and safety of ophthalmic surgery, which involves the delicate aspects of pain control, sedation, and patient response. Advances in ophthalmic surgery cause a need for exact and individualized anest...

Anesthesia depth prediction from drug infusion history using hybrid AI.

BMC medical informatics and decision making
BACKGROUND: Accurately predicting the depth of anesthesia is essential for ensuring patient safety and optimizing surgical outcomes. Traditional regression-based approaches often struggle to model the complex and dynamic nature of patient responses t...

Utilization of non-invasive ventilation before prehospital emergency anesthesia in trauma - a cohort analysis with machine learning.

Scandinavian journal of trauma, resuscitation and emergency medicine
BACKGROUND: For preoxygenation, German guidelines consider non-invasive ventilation (NIV) as a possible method in prehospital trauma care in the absence of aspiration, severe head or face injuries, unconsciousness, or patient non-compliance. As data ...

Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.

BMC medical informatics and decision making
BACKGROUND: Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool...