<![CDATA[The rising prevalence of non-communicable diseases requires innovative approaches to support early detection and preventive healthcare. This study aims to analyze the utilization of artificial intelligence–based health technologies for the early detection of non-communicable diseases within modern healthcare services. A mixed-method research design was employed, combining quantitative surveys with qualitative interviews, observations, and document analysis in selected healthcare facilities implementing artificial intelligence–supported screening systems. Quantitative data were analyzed using descriptive and inferential statistical techniques, while qualitative data were examined through content and interpretative analysis. The results show that artificial intelligence–based health technologies significantly improve screening efficiency, data processing speed, and perceived diagnostic accuracy, thereby supporting earlier identification of high-risk patients. Qualitative findings indicate that artificial intelligence assists clinical decision-making and enhances preventive care strategies, although challenges related to data quality, algorithm transparency, ethical concerns, and technological readiness remain. The discussion emphasizes that artificial intelligence should be positioned as a supportive tool integrated into clinical workflows rather than a substitute for professional judgment. This study contributes to digital health literature by providing empirical insights into real-world implementation of artificial intelligence in early disease detection and offers practical implications for healthcare institutions and policymakers in developing ethical, effective, and sustainable artificial intelligence–driven preventive healthcare systems.]]>
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