Introduction:
Introduction Cigarette smoking is the major cause of chronic obstructive pulmonary disease (COPD) and lung cancer. Tobacco smoke, which consist of solid and liquid particles and gases, has thousands of chemical components, including many well-characterized toxins and carcinogens. Big data, artificial intelligence (AI), machine learning is a promising tool that can predict disease very well.

Objectives:
Our aim was to assess the importance of big data and artificial intelligence in tobacco control.

Methods:
We used the NCBI PubMed database to search six keywords: COPD, chronic inflammation, lung cancer, smoking, big data and AI in the last six year’s research from 2011 to 2017. We found a total of 31 articles. Among these studies, we excluded HIV, CVD, and marijuana studies. A very few studies used the big data, AI concept, that identified these diseases.

Results:
Studies included: Multimodal e-Health services for smoking cessation used in the SmokeFreeBrain project and Genome-wide meta-analysis identifies smoking behavior among adults. One study shows ventilation/perfusion (V/P) single-photon emission computed tomography (SPECT) is recognized as a diagnostic method for the diagnosis of pulmonary embolism. Another study shows that the strength of hybrid imaging in patients with COPD and long-term tobacco smokers is primarily in detecting tumor-suspected changes and lung cancer.

Conclusion(s):
After reviewing all studies, we conclude that tobacco smoking is a risk factor for lung diseases which can be identified through statistical methods as well as machine learning or AI. These findings have been translated into easily consumable content. Compiling and applying Big Data techniques in tobacco control may identify more findings that can help in broader aspects for tobacco control.