Pan Yan, Jiao Fu-Yong
Department of Pediatrics, The First Affiliated Hospital of Yangtze University, Jingzhou 434000, Hubei Province, China.
Shaanxi Kawasaki Disease Diagnosis and Treatment Center, Shaanxi Provincial People's Hospital, Xi'an 710000, Shaanxi Province, China.
World J Clin Cases. 2024 Aug 16;12(23):5304-5307. doi: 10.12998/wjcc.v12.i23.5304.
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 . 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.
这篇社论对最近发表在《》上的一篇题为“ChatGPT与生成式人工智能在医学安全教育中的潜力与局限”的文章进行了评论。人工智能在川崎病(KD)领域的各种应用中具有巨大潜力。一是机器学习(ML)辅助KD诊断,全球已利用ML构建临床预测模型;二是使用基因信号计算工具箱识别KD,可用于监测疾病严重程度的关键临床特征和实验室参数;三是利用深度学习(DL)辅助心脏超声检测。DL算法在检测冠状动脉病变方面的表现与经验丰富的心脏专家相似,有助于KD的诊断。为了在KD的诊断和治疗过程中有效利用人工智能,利用更多医疗数据提高人工智能决策的准确性,同时解决患者个人信息保护和人工智能决策责任相关问题至关重要。预计人工智能的进展将为患者提供准确有效的医疗服务,这将对未来KD的诊断和治疗产生积极影响。