AI-driven Disease Prevention with a Special Emphasis on Respiratory Diseases and Pulmonary Public Health
DOI:
https://doi.org/10.64229/xzdeqv30Keywords:
Artificial intelligence, Respiratory disease prevention, Systems biology, Public health informatics, Disease surveillance, Ethical governanceAbstract
Artificial intelligence (AI) is increasingly transforming systems biology and preventive medicine by enabling the integration of multidimensional molecular, clinical, environmental, and population-level data to model complex biological networks underlying health and disease. In respiratory medicine, AI-driven network approaches have demonstrated significant potential for improving disease classification, biomarker discovery, and risk stratification in conditions such as chronic obstructive pulmonary disease (COPD), asthma, pulmonary fibrosis, and emerging respiratory infections. Integrated disease prevention in this context requires the convergence of molecular mechanisms, epidemiological insights, and public health strategies to address both individual susceptibility and population-level risk. AI-based frameworks facilitate the analysis of high-dimensional respiratory datasets, enhance disease surveillance and outbreak detection, support predictive modeling of environmental and lifestyle exposures (e.g., air pollution and smoking), and improve clinical decision-making and health communication. Despite these advances, substantial challenges remain, including data heterogeneity, algorithmic bias, privacy and ethical concerns, and unequal access to digital technologies, particularly in low- and middle-income countries. Addressing these limitations through ethical governance, context-specific AI model development, and capacity building is essential for advancing equitable, sustainable, and effective respiratory disease prevention and public health systems.
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