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:143
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.
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