Bellini V, Coccolini F, Forfori F, Bignami E. The artificial intelligence evidence-based medicine pyramid. World J Crit Care Med 2023; 12(2): 89-91 [PMID: 37034021 DOI: 10.5492/wjccm.v12.i2.89]
Corresponding Author of This Article
Elena Bignami, MD, Professor, Department of Medicine and Surgery, University of Parma, Anesthesiology, Critical Care and Pain Medicine Division, Viale Gramsci 14, Parma 43126, Italy. elenagiovanna.bignami@unipr.it
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Crit Care Med. Mar 9, 2023; 12(2): 89-91 Published online Mar 9, 2023. doi: 10.5492/wjccm.v12.i2.89
The artificial intelligence evidence-based medicine pyramid
Valentina Bellini, Federico Coccolini, Francesco Forfori, Elena Bignami
Valentina Bellini, Elena Bignami, Department of Medicine and Surgery, University of Parma, Anesthesiology, Critical Care and Pain Medicine Division, Parma 43126, Italy
Federico Coccolini, Department of General, Emergency and Trauma Surgery, Pisa University Hospital, Pisa 56124, Italy
Francesco Forfori, Department of Anesthesia and Intensive Care, University of Pisa, Pisa 53126, Italy
Author contributions: Bellini V, Coccolini F, Forfori F and Bignami E made substantial contributions to the conception and design of the study as well as the acquisition and interpretation of data; Bellini V and Bignami E drafted the article; Coccolini F and Forfori F made critical revisions related to important intellectual content within the manuscript; Bellini V, Coccolini F, Forfori F and Bignami E approved the final draft of the article.
Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Elena Bignami, MD, Professor, Department of Medicine and Surgery, University of Parma, Anesthesiology, Critical Care and Pain Medicine Division, Viale Gramsci 14, Parma 43126, Italy. elenagiovanna.bignami@unipr.it
Received: October 14, 2022 Peer-review started: October 14, 2022 First decision: November 14, 2022 Revised: November 21, 2022 Accepted: February 1, 2023 Article in press: February 1, 2023 Published online: March 9, 2023 Processing time: 143 Days and 19 Hours
Abstract
Several studies exist in the literature regarding the exploitation of artificial intelligence in intensive care. However, an important gap between clinical research and daily clinical practice still exists that can only be bridged by robust validation studies carried out by multidisciplinary teams.
Core Tip: Artificial intelligence (AI) use in intensive care is now a reality. However, there is still an important discrepancy between the results found in the scientific literature and the day-to-day clinical implementation of this technology. One reason for this is that the AI evidence pyramid in intensive care has only just begun to emerge. We need to focus on the next steps in AI pyramid evidence, amplifying the external validation of models and increasing the number of randomized clinical trials. Only robust validation studies carried out by multidisciplinary teams will help bridge this existing gap between clinical research and clinical practice.
