Technology-Enabled Monitoring and Artificial Intelligence Approaches for Assessing Tobacco Smoke Exposure and Respiratory Health Outcomes: A Scoping Review

Abstract

There is a growing global concern about the impact of tobacco smoke on our lungs. Therefore, it is important to identify, detect, and measure this impact to better understand it. However, it is also important to recognize that traditional methods such as questionnaires and observing smoking behaviour can have biases. With advances in technology, new methods for identifying smoking behaviour have emerged. A review paper, based on existing literature, investigates the role of technology—especially AI and ML—in understanding how tobacco smoke exposure affects the human respiratory system. This review follows the guidelines established by Joanna Briggs for review papers. The literature search was thorough, covering major databases like PubMed, Scopus, and Web of Science, using keywords such as "detection methods," "AI," "environmental sensors," and focusing on studies from 2020 to July 2025. Out of 36 articles identified, after removing duplicates and screening titles and abstracts, 9 articles remained that met the full-text review and inclusion criteria. These articles discuss detection techniques involving wearable sensors, ambient sensors, and predictive algorithms that enable continuous monitoring for accurate detection of tobacco exposure. Scientific evidence confirms the link between tobacco smoke exposure and respiratory diseases, including COPD, along with their symptoms and decreased lung function. Advances in AI and sensor technologies hold promise for the detection and identification of tobacco use and related diseases, including tobacco-related respiratory conditions.