Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases.

Expert review of respiratory medicine Pub Date : 2023-12-01 Epub Date: 2024-01-25 DOI:10.1080/17476348.2024.2302940
Joana Antão, Jeroen de Mast, Alda Marques, Frits M E Franssen, Martijn A Spruit, Qichen Deng
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Abstract

Introduction: Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment.

Areas covered: This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation.

Expert opinion: Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.

为管理阻塞性肺病患者的呼吸科临床医生揭开人工智能的神秘面纱。
导言:哮喘和慢性阻塞性肺病(COPD)是全球发病和死亡的主要原因。尽管有各种可用的诊断和治疗方法,但这些疾病仍给个人、经济和社会带来沉重负担。人工智能(AI)有望通过优化这些异质性复杂慢性呼吸系统疾病的诊断和治疗策略,为临床决策过程提供支持。人工智能的功能还可扩展到预测病情恶化风险、疾病进展和死亡率,为医疗保健专业人员提供有价值的见解,以实现更有效的护理。然而,呼吸科临床医生和数据科学家之间的知识差距仍然是人工智能广泛应用的主要制约因素,并可能阻碍未来的发展。本综述旨在弥合这一差距,通过解释人工智能在哮喘和慢性阻塞性肺疾病诊断和治疗中的方法和附加值,鼓励人工智能的应用:本综述概述了人工智能和机器学习的基本概念,概述了建立模型的关键步骤,举例说明了其在哮喘和慢性阻塞性肺病护理中的适用性,并讨论了其实施障碍:机器学习可以促进我们对哮喘和慢性阻塞性肺病的了解,实现个性化治疗和更好的疗效。需要进一步研究和验证,以确保开发出具有临床意义且可推广的模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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