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[重症医学中的人工智能]

[Artificial intelligence in intensive care medicine].

作者信息

Baumgart André, Beck Grietje, Ghezel-Ahmadi David

机构信息

Zentrum für Präventivmedizin und Digitale Gesundheit, Medizinische Fakultät Mannheim der Universität Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Deutschland.

Abteilung für Anästhesiologie, Intensivmedizin und Schmerzmedizin, Universitätsmedizin Mannheim gGmbH, Medizinische Fakultät Mannheim der Universität Heidelberg, Mannheim, Deutschland.

出版信息

Med Klin Intensivmed Notfmed. 2024 Apr;119(3):189-198. doi: 10.1007/s00063-024-01117-z. Epub 2024 Mar 28.

Abstract

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

摘要

近年来,人工智能(AI)在重症医学中的整合取得了显著进展,特别是在预测分析、并发症的早期检测以及决策支持系统的开发等领域。主要挑战仍然是数据的可用性和质量、偏差的减少以及算法和模型产生可解释结果的需求。解释这些系统的方法对于提高医疗保健专业人员和患者之间的信任、理解以及伦理考量至关重要。对医疗保健专业人员进行AI原理、术语、伦理考量和实际应用方面的适当培训对于AI的成功使用至关重要。仔细评估AI对患者自主权和数据保护的影响对于其在重症医学中的负责任使用至关重要。必须在伦理和实际考量之间保持平衡,以确保以患者为中心的护理,同时遵守数据保护法规。临床医生、AI工程师和监管机构之间的协同合作对于在重症医学中充分发挥AI的潜力并最大限度地提高其对患者护理的积极影响至关重要。未来的研发工作应专注于改进用于实时预测的AI模型,提高基于AI的闭环系统的准确性和实用性,并克服伦理、技术和监管挑战,特别是在生成式AI系统中。

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