Artificial Intelligence in Predicting Postoperative Surgical Complications

Kaushik Bhattacharya,Neela Bhattacharya,Sandeep Kumar,Vipul D. Yagnik,Pankaj Garg,Prema Ram Choudhary

Indian Journal of Surgery（2024）SCI 4区

Mata Gujri Memorial Medical College and LSK Hospital

Cited 0|Views7

Abstract

Artificial intelligence (AI), once integrated into medicine, has transformed its role from a mere statistical tool to a sophisticated system capable of guiding surgical procedures and predicting outcomes. AI algorithms utilize machine learning (ML), deep learning, and neural networks to analyze large datasets and identify patterns, ultimately making accurate predictions about patient outcomes. Through continuous learning, adaptation, and updating, these systems assist healthcare professionals in the diagnosis and offer real-time guidance during surgical interventions, reducing surgical error and postoperative complications. This evolution marks a paradigm shift, enhancing precision, efficiency, and, ultimately, patient care in surgery. This review article explores the critical role of AI in predicting surgical complications and discusses associated challenges and ethical considerations.

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Key words

Artificial intelligence,Machine learning,Postoperative complication,Tumor recurrence

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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【要点】：本文探讨了人工智能在预测术后手术并发症中的关键作用，强调了其在提高手术精准性和效率，以及提升患者护理方面的潜力，并讨论了相关挑战和伦理问题。

【方法】：文章回顾了人工智能算法，包括机器学习、深度学习和神经网络，如何分析大量数据集并识别模式，以准确预测患者结果。

【实验】：本文未提供具体的实验细节或使用的数据集名称及结果。