Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

Cannabis use and low socioeconomic status are recognized as distinct risk factors for cardiovascular pathologies. This retrospective cross-sectional study aims to investigate the association between income status and cardiovascular disease risk among young hospitalized adults with recreational cannabis use or cannabis use disorder (CUD). We analyzed young adult hospitalizations (18-44 years) with known CUD from the 2020 National Inpatient Sample. Propensity score-matched analysis (1:1) was used to compare comorbidities and major adverse cardiac and cerebrovascular events (MACCE) between the high-income quartile (HIQ) and low-income quartile (LIQ) within the CUD cohort. Out of the young adult admissions, a total of 286,855 individuals were identified as recreational cannabis users. After propensity matching, we divided the CUD cohort into 63,355 patients with HIQ (41.1% female) and LIQ (41.4% female). While both cohorts exhibited comparable ages and racial distribution (> 65% white), the individuals from the LIQ cohort were predominantly from the South (45.7% vs. 27.9%), visiting rural hospitals (13.9% vs. 1.3%), with Medicaid (56.3% vs. 41.1%) as the predominant payer. Additionally, the LIQ cohort had a higher prevalence of drug abuse (65.5% vs. 58.4%), tobacco use (24.9% vs. 22.6%), HTN (17% vs. 14.7%), chronic pulmonary disease (13.2% vs. 12.7%), DM (8% vs. 6.6%), and obesity (10.9% vs. 10.0%). Additionally, in-hospital outcomes like MACCE (2% vs. 0.8%) and acute ischemic stroke (AIS) (0.5% vs. 0.3%) were higher in the LIQ cohort (Graphical Abstract). After adjusting for confounding factors, the risk of AIS in the HIQ cohort was 37% lower than in the LIQ cohort (OR: 0.63 [95% CI: 0.42-0.96]). All p values < 0.05. Higher prevalence of CVD risk factors in the LIQ-CUD group and lower odds of AIS-related admissions in the HIQ-CUD group warrant targeted interventions and policy measures to understand and address the behavioral risk of cannabis use by economic status and disparities in MACCE.