The impact of artificial intelligence in improving the diagnosis and treatment of chronic diseases

Background: AI is being used more in healthcare to help diagnose and treat chronic diseases by looking at large sets of clinical data and making decisions for doctors. Its real-world effects, on the other hand, depend on how well it is used and built into health systems. Aim; The point of this study was to look at how AI can help diagnose and treat chronic diseases better and to find the technological, infrastructure, and policy factors that affect how well it works for doctors. Methodology: A structured questionnaire was sent to a group of healthcare professionals as part of a descriptive–correlational design. The test had three parts: using AI for diagnosis and treatment, putting it all together and managing it, and looking at clinical and patient outcomes and workflow. SPSS was used to look at the data. It did tests for validity and reliability, descriptive statistics, Pearson correlations, and multiple regression. Results: Overall, Cronbach's alpha = 0.929 showed that the scale was very reliable. A strong link existed between AI use and outcomes (r = 0.672, p < 0.001), as well as between implementation/governance and outcomes (r = 0.659, p < 0.001). We found that using AI (β = 0.426, p < 0.001) and putting it into practice and making sure it's governed (² = 0.414, p < 0.001) together explained 55.6% of the differences in clinical and patient outcomes. Conclusion: When the right infrastructure, interoperability, governance, and professional acceptance are in place, AI can make diagnosis, treatment, and workflow a lot better in managing chronic diseases. Recommendations: Health systems should put money into digital infrastructure that can work with other systems, clear rules and regulations, training for staff, and AI tools that are easy to understand and focus on the needs of patients. In the future, researchers should look into disease-specific, longitudinal, and cost-effectiveness outcomes.