Dr.Mehwish
AI plays a dual role in this context: supporting tobacco control efforts to promote public health and being leveraged by the tobacco industry to enhance production and marketing. Below, I’ll outline key applications of AI relevant to tobacco, focusing on both cessation and industry practices, while critically examining the implications.
•Monitoring Pro-Tobacco Content: AI, including computer vision and large language models (LLMs), is used to detect tobacco-promoting content on social media platforms.
• Public Health Insights: AI analyzes social media trends to understand vaping behaviors and inform policy. For instance, generative AI can process large datasets to monitor emerging tobacco trends, aiding evidence-based tobacco control strategies.
• Public Health vs. Industry Exploitation: While AI aids cessation, the tobacco industry’s use of AI to enhance marketing and production undermines WHO’s goals. For example, synthetic nicotine products evade FDA regulations, and AI helps promote these to youth via social media.
• Data Privacy: AI cessation tools collect user data, requiring strict privacy measures.
AI is transforming the detection of diseases linked to tobacco use by analyzing medical data, imaging, and behavioral patterns with high accuracy and speed. Below are the key ways AI is applied, based on current practices and research up to May 31, 2025.
1. Medical Imaging Analysis:
• Lung Cancer Detection:Screening and Triage in Low-Resource Settings:• Mobile AI Tools: In regions with limited access to radiologists, AI-powered mobile apps analyze cough sounds or basic imaging to screen for tobacco-related diseases. For instance, a 2024 pilot in India used AI to process smartphone-based lung function tests, achieving 80% accuracy in detecting COPD.
How AI Screens for Tobacco-Related Oral Cancer
AI leverages imaging, data analysis, and predictive modeling to detect oral cancer, particularly in populations with high tobacco use. Here’s a breakdown of the key approaches:
1. AI-Powered Imaging Analysis:
• Oral Lesion Detection:
• Intraoral Photography and Smartphone Imaging: AI uses convolutional neural networks (CNNs) to analyze images of the oral cavity (e.g., tongue, gums, cheeks) captured via intraoral cameras or smartphones. These models identify suspicious lesions, such as leukoplakia or erythroplakia, often linked to tobacco use. A 2024 study in Oral Oncology reported a CNN-based model achieving 92% sensitivity and 89% specificity in detecting oral squamous cell carcinoma (OSCC) from intraoral images.
We had provide a concise overview of AI tools and build various Solution’s will provide to healthcare industry soon for tobacco-related oral cancer screening, lung screening, skin disorders and industrial tools including existing technologies, key considerations for development, and potential innovations, while tying it to public health goals.
