Artificial intelligence and machine learning in respiratory medicine.
Evgeni MekovMarc MiravitllesRosen PetkovPublished in: Expert review of respiratory medicine (2020)
Introduction: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in particular in respiratory medicine is an increasingly relevant topic.Areas covered: We aimed to identify and describe the studies published on the use of AI and ML in the field of respiratory diseases. The string '(((pulmonary) OR respiratory)) AND ((artificial intelligence) OR machine learning)' was used in PubMed as a search strategy. The majority of studies identified corresponded to the area of chronic obstructive pulmonary disease (COPD), in particular to COPD and chest computed tomography scans, interpretation of pulmonary function tests, exacerbations and treatment. Another field of interest is the application of AI and ML to the diagnosis of interstitial lung disease, and a few other studies were identified on the fields of mechanical ventilation, interpretation of images on chest X-ray and diagnosis of bronchial asthma.Expert opinion: ML may help to make clinical decisions but will not replace the physician completely. Human errors in medicine are associated with large financial losses, and many of them could be prevented with the help of AI and ML. AI is particularly useful in the absence of conclusive evidence of decision-making.
Keyphrases
- artificial intelligence
- machine learning
- chronic obstructive pulmonary disease
- deep learning
- big data
- computed tomography
- interstitial lung disease
- mechanical ventilation
- lung function
- systemic sclerosis
- decision making
- case control
- dual energy
- endothelial cells
- intensive care unit
- rheumatoid arthritis
- cystic fibrosis
- acute respiratory distress syndrome
- primary care
- high resolution
- respiratory tract
- pulmonary hypertension
- emergency department
- positron emission tomography
- systematic review
- randomized controlled trial
- idiopathic pulmonary fibrosis
- healthcare
- patient safety
- adverse drug
- combination therapy
- clinical practice
- mass spectrometry
- electronic health record
- quality improvement
- air pollution
- contrast enhanced
- drug induced