Role of AI in Disease Prediction

Integrating artificial intelligence (AI) into healthcare has revolutionized disease prediction by providing tools capable of analyzing complex datasets and identifying patterns that may be invisible to humans. This paper reviews existing AI-based methods in disease prediction, including machine learning algorithms, deep learning models, and hybrid approaches. It highlights their applications across various diseases—such as cardiovascular diseases, diabetes, cancer, and neurological disorders—discussing each method's performance, advantages, and limitations. AI-driven methods significantly enhance both accuracy and scalability; however, they also introduce challenges related to data quality, interpretability, and ethical considerations. To address these issues, the review highlights crucial future directions, underscoring the urgent need for interpretable AI, tailored prediction models, and secure, robust AI frameworks that prioritize ethical standards.