{"title":"人工智能在预测腹腔镜胃癌根治术后并发症风险方面的进展:重大飞跃。","authors":"Hong-Niu Wang, Jia-Hao An, Liang Zong","doi":"10.3748/wjg.v30.i43.4669","DOIUrl":null,"url":null,"abstract":"<p><p>In a recent paper, Hong <i>et al</i> developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.</p>","PeriodicalId":23778,"journal":{"name":"World Journal of Gastroenterology","volume":"30 43","pages":"4669-4671"},"PeriodicalIF":4.3000,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572634/pdf/","citationCount":"0","resultStr":"{\"title\":\"Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.\",\"authors\":\"Hong-Niu Wang, Jia-Hao An, Liang Zong\",\"doi\":\"10.3748/wjg.v30.i43.4669\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In a recent paper, Hong <i>et al</i> developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.</p>\",\"PeriodicalId\":23778,\"journal\":{\"name\":\"World Journal of Gastroenterology\",\"volume\":\"30 43\",\"pages\":\"4669-4671\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-11-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11572634/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Gastroenterology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3748/wjg.v30.i43.4669\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Gastroenterology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3748/wjg.v30.i43.4669","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.
In a recent paper, Hong et al developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.
期刊介绍:
The primary aims of the WJG are to improve diagnostic, therapeutic and preventive modalities and the skills of clinicians and to guide clinical practice in gastroenterology and hepatology.