Artificial Intelligence For Early Disease Detection: Transforming Preventive Healthcare

Background: Preventive healthcare is undergoing a paradigm shift from reactive treatment to proactive health management. Early detection of diseases plays a crucial role in reducing mortality, minimizing treatment costs, and improving patients’ quality of life. In this context, Artificial Intelligence (AI) has emerged as a transformative force capable of identifying subtle, often imperceptible patterns in clinical and biomedical data. Objective: This paper explores how AI-driven technologies — including machine learning, deep learning, and medical image analysis — are revolutionizing the field of preventive healthcare by enabling early disease prediction and diagnosis. Methods: A structured review and synthesis of recent studies were conducted, focusing on AI models applied to early detection of major diseases such as cancer, cardiovascular conditions, and oral disorders. Case examples include convolutional neural networks (CNNs) for medical imaging, predictive models using electronic health records (EHRs), and generative AI for medical data augmentation. Results: Findings indicate that AI algorithms can detect disease indicators with accuracy levels surpassing traditional diagnostic methods. Integrating AI-based decision-support systems in clinical practice enhances physicians’ ability to make timely interventions and supports large-scale public health monitoring. Conclusion: Artificial Intelligence is transforming preventive healthcare by shifting the focus from treatment to prediction and prevention. The integration of AI into medical systems not only improves early disease detection but also redefines the future of health services, promoting personalized, data-driven, and sustainable healthcare for all.

کلیدواژه ها:

Artificial Intelligence ، Early Disease Detection ، Preventive Healthcare ، Machine Learning ، Medical Imaging ، Health Informatics