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危重症中的人工智能及其对患者护理的影响:一项全面综述。

Artificial intelligence in critical illness and its impact on patient care: a comprehensive review.

作者信息

Saqib Muhammad, Iftikhar Muhammad, Neha Fnu, Karishma Fnu, Mumtaz Hassan

机构信息

Khyber Medical College, Peshawar, Khyber Pakhtunkhwa, Pakistan.

Ghulam Muhammad Mahar Medical College, Sukkur, Sindh, Pakistan.

出版信息

Front Med (Lausanne). 2023 Apr 20;10:1176192. doi: 10.3389/fmed.2023.1176192. eCollection 2023.


DOI:10.3389/fmed.2023.1176192
PMID:37153088
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10158493/
Abstract

Artificial intelligence (AI) has great potential to improve the field of critical care and enhance patient outcomes. This paper provides an overview of current and future applications of AI in critical illness and its impact on patient care, including its use in perceiving disease, predicting changes in pathological processes, and assisting in clinical decision-making. To achieve this, it is important to ensure that the reasoning behind AI-generated recommendations is comprehensible and transparent and that AI systems are designed to be reliable and robust in the care of critically ill patients. These challenges must be addressed through research and the development of quality control measures to ensure that AI is used in a safe and effective manner. In conclusion, this paper highlights the numerous opportunities and potential applications of AI in critical care and provides guidance for future research and development in this field. By enabling the perception of disease, predicting changes in pathological processes, and assisting in the resolution of clinical decisions, AI has the potential to revolutionize patient care for critically ill patients and improve the efficiency of health systems.

摘要

人工智能(AI)在改善重症监护领域和提高患者治疗效果方面具有巨大潜力。本文概述了人工智能在危重病中的当前和未来应用及其对患者护理的影响,包括其在疾病感知、预测病理过程变化以及协助临床决策方面的应用。要实现这一点,重要的是要确保人工智能生成的建议背后的推理是可理解和透明的,并且人工智能系统在重症患者护理中设计得可靠且稳健。必须通过研究和制定质量控制措施来应对这些挑战,以确保人工智能以安全有效的方式使用。总之,本文强调了人工智能在重症监护中的众多机遇和潜在应用,并为该领域的未来研究和发展提供了指导。通过实现疾病感知、预测病理过程变化以及协助解决临床决策,人工智能有可能彻底改变重症患者的护理方式并提高卫生系统的效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/235897c31355/fmed-10-1176192-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/3dbc7346b791/fmed-10-1176192-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/41d4dc123ee3/fmed-10-1176192-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/235897c31355/fmed-10-1176192-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/3dbc7346b791/fmed-10-1176192-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/41d4dc123ee3/fmed-10-1176192-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cccd/10158493/235897c31355/fmed-10-1176192-g003.jpg

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本文引用的文献

[1]
Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.

JMIR Perioper Med. 2023-1-27

[2]
Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010-2021.

J Pers Med. 2022-12-27

[3]
Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care.

Front Cardiovasc Med. 2022-10-14

[4]
Explainable Artificial Intelligence for Predicting Hospital-Acquired Pressure Injuries in COVID-19-Positive Critical Care Patients.

Comput Inform Nurs. 2022-10-1

[5]
Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach.

J Pers Med. 2022-4-20

[6]
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead.

Nat Mach Intell. 2019-5

[7]
Artificial Intelligence in Critical Care Medicine.

Crit Care. 2022-3-22

[8]
Computer-assisted Individualized Hemodynamic Management Reduces Intraoperative Hypotension in Intermediate- and High-risk Surgery: A Randomized Controlled Trial.

Anesthesiology. 2021-8-1

[9]
Deep Learning to Quantify Pulmonary Edema in Chest Radiographs.

Radiol Artif Intell. 2021-1-6

[10]
Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence.

J Pers Med. 2021-4-2

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