BACKGROUND: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE: The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS: We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS: The resulting articles were assessed and organized into four basic contextual categories, namely: i) 'COPD diagnosis', ii) 'COPD prognosis', iii) 'Patient classification', iv) 'COPD management', and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS: We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.
BACKGROUND: Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE: The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS: We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS: The resulting articles were assessed and organized into four basic contextual categories, namely: i) 'COPD diagnosis', ii) 'COPD prognosis', iii) 'Patient classification', iv) 'COPD management', and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS: We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.