AI-Driven Early Detection Models for Chronic Diseases in Saudi Arabia: A Systematic Review and Translational Framework

Background : In Saudi Arabia, chronic health issues like diabetes, heart disease, and cancer are on the rise. They lead to avoidable complications and result in higher healthcare expenses. By utilizing clinical data from EHRs and lab results, machine learning can play a significant role in the prevention of potential problems. Objective : The present review aims to provide research articles from 2020-2025 on how artificial intelligence and machine learning can contribute to the early detection or prediction of risks of developing diabetes, heart disease, and cancer. In addition, it aims to provide a framework that can be applied in the present reporting and evaluation standards. Methods : The present review was done based on the PRISMA 2020-2025 guidelines, including literature from MEDLINE/PubMed, Scopus, IEEE Xplore, and Google Scholar databases from January 2020 to December 2025. Results : The evolution of research from small tables of data to bigger populations from EHRs, utilizing feature engineering, and decision tree algorithms was observed. In Saudi Arabia, diabetes research was initially based on modeling disease progression and age of onset, but it now includes modeling disease onset and progression. Cardiovascular disease research was based on stratification and helping in the early diagnosis of the disease. Conclusion : The potential of artificial intelligence and machine learning in the early detection of diabetes, heart disease, and cancer can be promising. However, it would require better governance and evaluation.