Editorial: Transforming chronic disease treatment with AI and big data

population-level evidence and standardized protocols, struggle to address the nuanced, individual-specific dynamics inherent to conditions such as cancer, diabetes, cardiovascular disorders, and metabolic liver diseases. The integration of Artificial Intelligence (AI) and Big Data analytics is heralding a transformative shift, moving healthcare from a reactive, one-size-fits-all model toward proactive, precise, and personalized care. The collection of studies summarized herein exemplifies this paradigm shift, showcasing innovative applications of computational tools across diverse chronic conditions.A central theme across these works is the leveraging of high-throughput omics data and clinical records to discover novel biomarkers and elucidate disease mechanisms. In liver hepatocellular carcinoma (LIHC), the identification of DNASE1L3 as a stage-associated biomarker through integrated transcriptomic and clinical data analysis provides a tangible tool for refining prognostic assessment (Xue et al., 2025). Similarly, in clear cell renal cell carcinoma (ccRCC), bioinformatics pipelines pinpointed CCL5, LOX, C3, and PLG as key genes linked to tumor The scope of AI applications extends to optimizing therapeutic strategies and understanding treatment mechanisms. In primary aldosteronism, AI models show promise in improving screening, diagnostic specificity, subtype classification, and predicting treatment responses (Xu, et al., 2025). Meanwhile, an integrative approach combining network pharmacology, bioinformatics, and in vivo validation elucidated the multi-target, anti-inflammatory mechanism of the traditional Chinese medicine Qinghuo Rougan Formula in uveitis, bridging traditional knowledge with modern systems biology (Jing et al., 2025).Despite the remarkable progress illustrated by these studies, significant challenges remain on the path to clinical integration. Key issues include the need for larger, multicenter datasets to improve model generalizability, the "black-box" nature of some complex AI algorithms requiring enhanced interpretability, and the necessity for rigorous real-world validation in diverse clinical settings. Future research must prioritize the development of robust, transparent, and ethically deployed AI tools that complement clinical expertise.In conclusion, the convergence of AI and Big Data is fundamentally reshaping the landscape of chronic disease research and management. The works highlighted herespanning biomarker discovery, predictive modeling, mechanistic elucidation, and therapeutic optimization-collectively chart a course toward a future where healthcare is increasingly predictive, preventive, personalized, and participatory. As computational methodologies continue to evolve and integrate with multimodal health data, their potential to improve patient outcomes, enhance clinical decision-making, and reduce the overall burden of chronic diseases will only expand. The journey from populationbased paradigms to truly personalized medicine is well underway, driven by the innovative fusion of clinical insight and technological prowess. Dai, R., Lei, X., Liu, X., Bian, C. Comprehensive bioinformatics analysis of the common mechanism of atherosclerosis and atrial fibrillation: emphasizing mitochondrial metabolic disorder and immune inflammation. Front. Mol. Biosci. 12, 1595048. doi: 10.3389/fmolb.2025.1595048. Xie, Z., Feng, C., Hong, Y., Chen, L., Li, M., Deng, W. (2025)