The artificial intelligence revolution in gastric cancer management: clinical applications

癌症 医学 重症监护医学 计算机科学 内科学
作者
Runze Li,Jingfan Li,Yuman Wang,Xiaoyu Liu,Weichao Xu,Rao Sun,Bing Xue,Xinqian Zhang,Yi Ai,Yong Du,Jianming Jiang
出处
期刊:Cancer Cell International [BioMed Central]
卷期号:25 (1) 被引量:2
标识
DOI:10.1186/s12935-025-03756-4
摘要

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
顺顺黎黎完成签到,获得积分10
1秒前
Nirvana完成签到 ,获得积分10
1秒前
酷酷水之完成签到,获得积分10
2秒前
2秒前
柴柴完成签到,获得积分10
2秒前
廿一完成签到,获得积分10
3秒前
睡觉觉了完成签到,获得积分10
3秒前
喜文完成签到,获得积分10
3秒前
FashionBoy应助zwf123采纳,获得10
3秒前
3秒前
3秒前
yu发布了新的文献求助10
3秒前
white完成签到 ,获得积分10
3秒前
3秒前
123发布了新的文献求助30
4秒前
墟里烟完成签到,获得积分20
4秒前
FashionBoy应助是康康呀采纳,获得10
4秒前
ylf完成签到,获得积分10
4秒前
4秒前
4秒前
5秒前
raininjuly应助xdc采纳,获得10
5秒前
6秒前
达尔文发布了新的文献求助10
6秒前
小陈陈发布了新的文献求助10
6秒前
ylf发布了新的文献求助10
7秒前
kk完成签到,获得积分0
7秒前
youhui完成签到,获得积分20
8秒前
wang完成签到,获得积分10
8秒前
zzzzlll发布了新的文献求助10
8秒前
Jason完成签到,获得积分10
8秒前
英俊的铭应助王wang采纳,获得10
8秒前
开朗季节发布了新的文献求助10
8秒前
9秒前
9秒前
Ava应助紫色茄子采纳,获得10
10秒前
10秒前
王悦靓发布了新的文献求助10
10秒前
听不清的耳语完成签到,获得积分10
10秒前
tester_gater发布了新的文献求助20
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Organometallic Chemistry of the Transition Metals 800
Chemistry and Physics of Carbon Volume 18 800
The Organometallic Chemistry of the Transition Metals 800
The formation of Australian attitudes towards China, 1918-1941 640
Signals, Systems, and Signal Processing 610
全相对论原子结构与含时波包动力学的理论研究--清华大学 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6441329
求助须知:如何正确求助?哪些是违规求助? 8255321
关于积分的说明 17576538
捐赠科研通 5499960
什么是DOI,文献DOI怎么找? 2900171
邀请新用户注册赠送积分活动 1876951
关于科研通互助平台的介绍 1717026