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

Alan Kaplan,Hui Cao,J Mark Fitzgerald,Nick Iannotti,Eric Yang,Janwillem W.H Kocks,Konstantinos Kostikas,David Price,Helen K Reddel,Ioanna Tsiligianni,Claus F Vogelmeier,Pascal Pfister,Paul Mastoridis

doi:10.1016/j.jaip.2021.02.014

Abstract

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.

Highlights

The terms artificial intelligence (AI), machine learning, and deep learning are often used interchangeably but are hierarchical
Irrespective of whether the clinicians’ performance was underestimated, this study showed that AI has a potential role in respiratory medicine that is beyond that of image analysis
These findings suggest that AI/machine learning offers an innovative approach to develop diagnostic algorithms that have the potential to aid diagnosis and differentiation of—among other conditions/diseases—respiratory diseases

Summary

INTRODUCTION

The terms artificial intelligence (AI), machine learning, and deep learning are often used interchangeably but are hierarchical. When errors do occur with AI, they mostly result from issues that arise during the learning step, usually poor quality training data or an irrelevant evaluation metric.[21] it is essential to ensure that the data set expresses the complete range of data and the real associations between them, that it does not contain misclassified examples, and does not present any bias that could lead the AI to learn false assumptions.[22] Other sources of error include the use of an inappropriate AI model for the learning process and stopping learning too early in the process.[22] Clinicians, AI researchers, as well as developers of AI applications and devices should work together to accelerate progress and to limit adverse consequences of applying AI in health care.[23] Rigorous translation pipelines will be needed to support their work This technology can optimize human intelligence to improve decision making and operational processes. DNNs can be trained to recognize specific pathologies on chest radiographs including tuberculosis,[28,29,30] malignant pulmonary nodules,[31] congestive

AI IN THE DIAGNOSIS OF ASTHMA AND CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Findings

CONCLUSIONS