Artificial Intelligence Integration in Neonatal Surgery for Enhancing Precision and Outcomes through Advanced Algorithmic Approaches

Abstract

Neonatal surgery has been transformed by artificial intelligence (AI), which has improved intraoperative decision-making,surgical planning, and diagnostic precision. By assessing popular machine learning and deep learning methods likeConvolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Reinforcement Learning models, this studyinvestigates the incorporation of AI in newborn surgical operations. Analysing existing AI-assisted newborn surgicalsystems, determining their shortcomings, and suggesting an ideal hybrid AI model that combines Reinforcement Learningand Transformer-based vision models for real-time decision support are all part of the research process. Using importantperformance measures such as accuracy, precision, recall, computational economy, and real-time adaptation, the suggestedmodel is compared to current techniques. According to experimental results, compared to traditional AI-assisted approaches,surgical precision can be improved by 15-20%, anomaly detection can be improved by 25%, and surgical time can bedecreased by 10%. The results highlight the potential of AI-powered neonatal surgery to improve patient outcomes, reducerisks, and establish a new standard for paediatric surgery.

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