Editorial
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Aug 16, 2024; 12(23): 5304-5307
Published online Aug 16, 2024. doi: 10.12998/wjcc.v12.i23.5304
Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease
Yan Pan, Fu-Yong Jiao
Yan Pan, Department of Pediatrics, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, Hubei Province, China
Fu-Yong Jiao, Shaanxi Kawasaki Disease Diagnosis and Treatment Center, Shaanxi Provincial People's Hospital, Xi’an 710000, Shaanxi Province, China
Author contributions: Jiao FY designed the study; Pan Y edited the manuscript significantly; Pan Y reviewed literature and provided input in writing the paper.
Conflict-of-interest statement: There is no conflict of interest associated with any of the senior authors or other coauthors who contributed their efforts to this manuscript.
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: Fu-Yong Jiao, MD, Research Scientist, Shaanxi Kawasaki Disease Diagnosis and Treatment Center, Shaanxi Provincial People's Hospital, No. 256 West Youyi Road, Xi’an 710000, Shaanxi Province, China. 3105089948@qq.com
Received: April 11, 2024
Revised: May 15, 2024
Accepted: June 4, 2024
Published online: August 16, 2024
Processing time: 84 Days and 23.3 Hours
Abstract

This editorial provides commentary on an article titled "Potential and limitations of ChatGPT and generative artificial intelligence (AI) in medical safety education" recently published in the World Journal of Clinical Cases. AI has enormous potential for various applications in the field of Kawasaki disease (KD). One is machine learning (ML) to assist in the diagnosis of KD, and clinical prediction models have been constructed worldwide using ML; the second is using a gene signal calculation toolbox to identify KD, which can be used to monitor key clinical features and laboratory parameters of disease severity; and the third is using deep learning (DL) to assist in cardiac ultrasound detection. The performance of the DL algorithm is similar to that of experienced cardiac experts in detecting coronary artery lesions to promoting the diagnosis of KD. To effectively utilize AI in the diagnosis and treatment process of KD, it is crucial to improve the accuracy of AI decision-making using more medical data, while addressing issues related to patient personal information protection and AI decision-making responsibility. AI progress is expected to provide patients with accurate and effective medical services that will positively impact the diagnosis and treatment of KD in the future.

Keywords: Artificial intelligence; Kawasaki disease; Diagnosis; Prediction; Image

Core Tip: Artificial intelligence (AI) holds transformative potential in the diagnosis and treatment of Kawasaki Disease (KD). Utilizing machine learning algorithms, AI can analyze complex biomarkers to enhance diagnostic accuracy. Gene signal calculation tools can differentiate KD from similar inflammatory conditions, while deep learning algorithms can improve the precision of cardiac ultrasound detection. However, successful integration of AI into clinical practice requires addressing challenges such as data privacy, ethical considerations, and the need for robust, diverse datasets to ensure the reliability and accountability of AI systems.