Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis

背景(考古学) 医学 哮喘 人工智能 慢性阻塞性肺病 呼吸内科 机器学习 重症监护医学 计算机科学 内科学 外科 古生物学 生物
作者
Alan Kaplan,Hui Cao,J. Mark FitzGerald,Nick Iannotti,Eric Yang,Janwillem Kocks,Κonstantinos Κostikas,David Price,Helen K. Reddel,Ioanna Tsiligianni,Claus Vogelmeier,Pascal Pfister,Paul Mastoridis
出处
期刊:The Journal of Allergy and Clinical Immunology: In Practice [Elsevier BV]
卷期号:9 (6): 2255-2261 被引量:189
标识
DOI:10.1016/j.jaip.2021.02.014
摘要

Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.
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
刚刚
Anderson发布了新的文献求助10
刚刚
会爬树的鱼完成签到,获得积分10
1秒前
koka发布了新的文献求助10
1秒前
机智免免完成签到 ,获得积分10
2秒前
Running发布了新的文献求助10
3秒前
二分三分完成签到,获得积分0
4秒前
Lawfy发布了新的文献求助10
4秒前
5秒前
6秒前
6秒前
7秒前
Georges-09完成签到,获得积分10
7秒前
lin发布了新的文献求助10
7秒前
木木关注了科研通微信公众号
9秒前
Eternal完成签到,获得积分10
10秒前
丘比特的应助被组蛋白采纳,获得10
10秒前
Jack发布了新的文献求助10
10秒前
ZZzzz完成签到,获得积分10
11秒前
王馨月发布了新的文献求助10
11秒前
BCLee完成签到,获得积分10
11秒前
11秒前
11秒前
123456发布了新的文献求助10
11秒前
既望完成签到,获得积分10
13秒前
14秒前
17秒前
bkagyin的应助被WX采纳,获得10
18秒前
小懒完成签到,获得积分10
18秒前
Yinxb的应助被燕子采纳,获得20
19秒前
HOHO发布了新的文献求助10
20秒前
amnsydglm完成签到,获得积分10
20秒前
春秋蝉完成签到 ,获得积分10
20秒前
22秒前
v0id的应助被wch666采纳,获得10
23秒前
StJoe发布了新的文献求助30
24秒前
24秒前
ww发布了新的文献求助10
25秒前
25秒前
在水一方的应助被王馨月采纳,获得10
26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
Aspects of Post-SPE Phonology 2000
Nanocomposite Removal of Pesticides from Wastewater 1000
Rosenblum, Global Change Biology 800
Final Report for the 2-D Test of Model 901 Rotor and Wing Airfoils (BSWT 592) 528
Berberine regulates the TLR4 signaling pathway to suppress hypoxia-induced proliferation and migration of pulmonary arterial smooth muscle cells 520
Organizational Behavior 510
热门求助领域 (近24小时)
化学 材料科学 医学 生物 计算机科学 工程类 纳米技术 物理 有机化学 化学工程 内科学 生物化学 复合材料 催化作用 心理学 细胞生物学 无机化学 电极 光电子学 人工智能
热门帖子
关注 科研通微信公众号,转发送积分 7895813
求助须知:如何正确求助?哪些是违规求助? 9404708
关于积分的说明 20865595
捐赠科研通 7498955
什么是DOI,文献DOI怎么找? 3359327
关于科研通互助平台的介绍 2497546
邀请新用户注册赠送积分活动 2388645