Khawar Muhammad Muneeb, Abdus Saboor Hafiz, Eric Rahul, Arain Nimra R, Bano Saira, Mohamed Abaker Mawada B, Siddiqui Batool I, Figueroa Reynaldo R, Koppula Srija R, Fatima Hira, Begum Afreen, Anwar Sana, Khalid Muhammad U, Jamil Usama, Iqbal Javed
King Edward Medical University, Rana Nursery Okara.
King Edward Medical University, Lahore, Pakistan.
Ann Med Surg (Lond). 2024 Oct 22;86(12):7202-7211. doi: 10.1097/MS9.0000000000002673. eCollection 2024 Dec.
Being an extremely high mortality rate condition, cardiac arrest cases have rightfully been evaluated via various studies and scoring factors for effective resuscitative practices and neurological outcomes postresuscitation. This narrative review aims to explore the role of artificial intelligence (AI) in predicting neurological outcomes postcardiac resuscitation. The methodology involved a detailed review of all relevant recent studies of AI, different machine learning algorithms, prediction tools, and assessing their benefit in predicting neurological outcomes in postcardiac resuscitation cases as compared to more traditional prognostic scoring systems and tools. Previously, outcome determining clinical, blood, and radiological factors were prone to other influencing factors like limited accuracy and time constraints. Studies conducted also emphasized that to predict poor neurological outcomes, a more multimodal approach helped adjust for confounding factors, interpret diverse datasets, and provide a reliable prognosis, which only demonstrates the need for AI to help overcome challenges faced. Advanced machine learning algorithms like artificial neural networks (ANN) using supervised learning by AI have improved the accuracy of prognostic models outperforming conventional models. Several real-world cases of effective AI-powered algorithm models have been cited here. Studies comparing machine learning tools like XGBoost, AI Watson, hyperspectral imaging, ChatGPT-4, and AI-based gradient boosting have noted their beneficial uses. AI could help reduce workload, healthcare costs, and help personalize care, process vast genetic and lifestyle data and help reduce side effects from treatments. Limitations of AI have been covered extensively in this article, including data quality, bias, privacy issues, and transparency. Our objectives should be to use more diverse data sources, use interpretable data output giving process explanation, validation method, and implement policies to safeguard patient data. Despite the limitations, the advancements already made by AI and its potential in predicting neurological outcomes in postcardiac resuscitation cases has been quite promising and boosts a continually improving system, albeit requiring close human supervision with training and improving models, with plans to educate clinicians, the public and sharing collected data.
心脏骤停是一种死亡率极高的病症，因此人们通过各种研究和评分因素对其进行了合理评估，以实现有效的复苏措施及复苏后良好的神经学转归。本叙述性综述旨在探讨人工智能（AI）在预测心脏复苏后神经学转归方面的作用。该方法涉及详细回顾近期所有关于AI、不同机器学习算法、预测工具的相关研究，并评估它们与更传统的预后评分系统和工具相比，在预测心脏复苏后病例神经学转归方面的益处。以前，决定转归的临床、血液和放射学因素容易受到其他影响因素的干扰，如准确性有限和时间限制。所开展的研究还强调，为了预测不良神经学转归，采用更多的多模式方法有助于调整混杂因素、解读多样的数据集并提供可靠的预后，这仅表明需要AI来帮助克服所面临的挑战。像人工神经网络（ANN）这样的先进机器学习算法通过AI的监督学习提高了预后模型的准确性，优于传统模型。本文引用了几个有效的AI驱动算法模型的实际案例。比较XGBoost、AI Watson、高光谱成像、ChatGPT-4和基于AI的梯度提升等机器学习工具的研究已指出了它们的有益用途。AI有助于减轻工作量、降低医疗成本、实现个性化护理、处理大量的基因和生活方式数据，并有助于减少治疗的副作用。本文广泛讨论了AI的局限性，包括数据质量、偏差、隐私问题和透明度。我们的目标应该是使用更多样化的数据源，使用可解释的数据输出并给出过程解释、验证方法，并实施保护患者数据的政策。尽管存在局限性，但AI在心脏复苏后病例神经学转归预测方面已经取得的进展及其潜力非常有前景，并推动了一个不断改进的系统，尽管这需要在训练和改进模型时进行密切的人工监督，同时计划对临床医生、公众进行教育并共享收集的数据。
