Key result
AI shows potential to revolutionize neonatal anesthesia via predictive analytics and automated monitoring systems.
While AI offers promising advancements for predicting complications and optimizing neonatal anesthesia, widespread clinical implementation requires overcoming significant data, ethical, and physiological challenges.
Should not yet change neonatal anesthesia practice; leaves open the need for prospective validation addressing data and ethical barriers.
Artificial intelligence (AI) refers to the capability of computing algorithms to mimic the decision-making capacity of human beings. At the same time, machine learning (ML), a subset of AI, allows the machine to learn from data without explicit programming. Over the recent decade, these technologies have profoundly impacted personalized diagnostics, drug discovery, therapeutics, and medical imaging, and their potential extends to understanding diseases and treatment effectiveness’ in pediatric and neonatal care. AI has emerged as a transformative force in modern medical practice, offering innovative solutions across various domains, including anesthesiology. ML algorithms are utilized to classify data and make predictions using supervised ML, which uses labelled datasets to train algorithms for tasks such as prognostic applications, and unsupervised ML, which explores data with minimal human guidance.
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Gupta et al. (2025) conducted a review in Neonatal anesthesia. Artificial Intelligence was evaluated. Artificial intelligence has the potential to revolutionize neonatal anesthesia by enhancing precision, safety, and individualized care through predictive analytics and automated monitoring systems.
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