Machine learning (ML) has increasingly been applied in preventive health care, allowing for the early detection of diseases and risk factors. For instance, a study by Flaxman et al. (2018) emphasizes how ML can help automate health prediction tasks that traditionally require human labor, enhancing the accuracy of population health models. Privacy-preserving methods like differential privacy have also been introduced to handle sensitive data without compromising individual confidentiality.

AI has the potential to increase health literacy by providing personalized information and recommendations, promoting positive health behaviors. Tools such as chatbots and decision-support systems have been designed to simplify complex health information, particularly for marginalized groups who may experience health disparities. AI models trained on specific populations can help tailor health interventions and improve the comprehension of health information.

AI applications in health behavior change, such as mobile health apps and predictive analytics, help users modify unhealthy behaviors by providing real-time feedback and interventions. AI-driven approaches can tailor strategies based on individual behaviors, making them particularly effective in personalized preventive care. Research shows that AI can facilitate sustainable behavior change by addressing social determinants of health, including lifestyle and environmental factors.

AI and ML can help address health disparities faced by LGBTQ+ and other marginalized communities by analyzing large-scale data to identify unique health challenges and disparities. AI-based models can predict health risks specific to these communities and help create inclusive, non-discriminatory health interventions. For example, AI can improve the understanding of the social and environmental factors affecting these groups’ health.

AI and ML are valuable in studying how environmental and ecological factors affect health outcomes. ML models can be trained to analyze the complex interactions between environmental exposures (e.g., pollution, climate change) and health conditions. These insights can be applied to predict and mitigate the effects of adverse environmental factors on health.