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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.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.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.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.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.

Introduction: Pulmonary embolism (PE) is a common life-threatening condition. Underdiagnosis and delays in the diagnosis are common, mainly due to unspecific clinical presentations. Aim: We used clinical parameters to train a Machine Learning algorithm for the diagnosis of PE in Emergency Department settings. Methods: We analyzed data from 190 patients that underwent Computed Tomography Pulmonary Angiogram (CTPA) due to clinical suspicion of PE in two tertiary hospitals. Gold standard was the diagnosis of pulmonary embolism in the CTPA scan. Using this data, we trained an algorithm in order to discern between patients with and without PE. Results: The best performance was achieved by the AdaBoost classification algorithm coupled with the Wrapper feature selection technique. In Table 1 we report accuracy, sensitivity and specificity of the proposed algorithm compared to some of the validated prediction rules for the assessment of pretest probability of PE (i.e. Wells score, Wells simplified, revised Geneva, revised Geneva simplified), on the same patient set. Conclusion: The proposed algorithm outperforms current scoring systems, especially in terms of accuracy and sensitivity; however, further training and validation with richer datasets is needed in order to assess its generalization capability. The incorporation of laboratory parameters as input to the algorithm is expected to enhance the predictive accuracy.