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Artificial intelligence in intensive care: are we there yet?

作者信息

Komorowski Matthieu

机构信息

Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK.

出版信息

Intensive Care Med. 2019 Sep;45(9):1298-1300. doi: 10.1007/s00134-019-05662-6. Epub 2019 Jun 24.

DOI:10.1007/s00134-019-05662-6

PMID:31236638

Abstract

摘要

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Artificial intelligence in intensive care: are we there yet?

作者信息

Komorowski Matthieu

机构信息

Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, SW7 2AZ, UK.

出版信息

Intensive Care Med. 2019 Sep;45(9):1298-1300. doi: 10.1007/s00134-019-05662-6. Epub 2019 Jun 24.

DOI:10.1007/s00134-019-05662-6

PMID:31236638

Abstract

摘要

相似文献

Artificial intelligence in intensive care: are we there yet?

Intensive Care Med. 2019 Sep;45(9):1298-1300. doi: 10.1007/s00134-019-05662-6. Epub 2019 Jun 24.

Artificial intelligence in the intensive care unit.

Crit Care. 2019 Jan 10;23(1):7. doi: 10.1186/s13054-018-2301-9.

Artificial Intelligence in Critical Care.

Int Anesthesiol Clin. 2019 Spring;57(2):89-102. doi: 10.1097/AIA.0000000000000221.

Artificial Intelligence for clinical decision support in Critical Care, required and accelerated by COVID-19.

Anaesth Crit Care Pain Med. 2020 Dec;39(6):691-693. doi: 10.1016/j.accpm.2020.09.010. Epub 2020 Oct 21.

Artificial intelligence in acute medicine: a call to action.

Crit Care. 2024 Jul 29;28(1):258. doi: 10.1186/s13054-024-05034-7.

The future of AI in critical care is augmented, not artificial, intelligence.

Crit Care. 2020 Dec 2;24(1):673. doi: 10.1186/s13054-020-03404-5.

Managing expectations of artificial intelligence.

Nature. 2018 Nov;563(7733):S141-S143. doi: 10.1038/d41586-018-07504-9.

Better medicine through machine learning: What's real, and what's artificial?

PLoS Med. 2018 Dec 31;15(12):e1002721. doi: 10.1371/journal.pmed.1002721. eCollection 2018 Dec.

Is artificial intelligence (AI) at the doorstep of Intensive Care Units (ICU) and operating room (OR)?

Anaesth Crit Care Pain Med. 2019 Aug;38(4):337-338. doi: 10.1016/j.accpm.2019.05.003. Epub 2019 May 15.

Feasibility of machine integrated point of care lung ultrasound automatic B-lines tool in the Corona-virus 2019 critical care unit.

Crit Care. 2021 Sep 24;25(1):345. doi: 10.1186/s13054-021-03770-8.

引用本文的文献

Artificial Intelligence in Critical Care: Promise, Peril, and the Path Forward.

Cureus. 2025 Jun 4;17(6):e85323. doi: 10.7759/cureus.85323. eCollection 2025 Jun.

Building a house without foundations? A 24-country qualitative interview study on artificial intelligence in intensive care medicine.

BMJ Health Care Inform. 2024 Apr 19;31(1):e101052. doi: 10.1136/bmjhci-2024-101052.

Causal inference using observational intensive care unit data: a scoping review and recommendations for future practice.

NPJ Digit Med. 2023 Nov 27;6(1):221. doi: 10.1038/s41746-023-00961-1.

Discrepancy between perceptions and acceptance of clinical decision support Systems: implementation of artificial intelligence for vancomycin dosing.

BMC Med Inform Decis Mak. 2023 Aug 11;23(1):157. doi: 10.1186/s12911-023-02254-9.

Prediction algorithm for ICU mortality and length of stay using machine learning.

Sci Rep. 2022 Jul 28;12(1):12912. doi: 10.1038/s41598-022-17091-5.

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.

Front Pediatr. 2022 May 10;10:864755. doi: 10.3389/fped.2022.864755. eCollection 2022.

ERS International Congress 2021: highlights from the Respiratory Intensive Care Assembly.

ERJ Open Res. 2022 May 23;8(2). doi: 10.1183/23120541.00016-2022. eCollection 2022 Apr.

Deep Learning-Based Pain Classifier Based on the Facial Expression in Critically Ill Patients.

Front Med (Lausanne). 2022 Mar 17;9:851690. doi: 10.3389/fmed.2022.851690. eCollection 2022.

Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.

BMJ Health Care Inform. 2022 Feb;29(1). doi: 10.1136/bmjhci-2021-100495.

Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.

JMIR Med Inform. 2022 Jan 19;10(1):e29434. doi: 10.2196/29434.

本文引用的文献

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis.

JAMA. 2019 May 28;321(20):2003-2017. doi: 10.1001/jama.2019.5791.

High-performance medicine: the convergence of human and artificial intelligence.

Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7.

Guidelines for reinforcement learning in healthcare.

Nat Med. 2019 Jan;25(1):16-18. doi: 10.1038/s41591-018-0310-5.

Transcriptomic Signatures in Sepsis and a Differential Response to Steroids. From the VANISH Randomized Trial.

Am J Respir Crit Care Med. 2019 Apr 15;199(8):980-986. doi: 10.1164/rccm.201807-1419OC.

The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care.

Nat Med. 2018 Nov;24(11):1716-1720. doi: 10.1038/s41591-018-0213-5. Epub 2018 Oct 22.

Six subphenotypes in septic shock: Latent class analysis of the PROWESS Shock study.

J Crit Care. 2018 Oct;47:70-79. doi: 10.1016/j.jcrc.2018.06.012. Epub 2018 Jun 8.

An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

Crit Care Med. 2018 Apr;46(4):547-553. doi: 10.1097/CCM.0000000000002936.

What's new in ICU in 2050: big data and machine learning.

Intensive Care Med. 2018 Sep;44(9):1524-1527. doi: 10.1007/s00134-017-5034-3. Epub 2017 Dec 26.

Intensive care medicine in 2050: the ICU in vivo.

Intensive Care Med. 2017 Nov;43(11):1700-1702. doi: 10.1007/s00134-017-4808-y. Epub 2017 May 8.

Translating Artificial Intelligence Into Clinical Care.

JAMA. 2016 Dec 13;316(22):2368-2369. doi: 10.1001/jama.2016.17217.