The integration of artificial intelligence (AI) in disease modeling presents significant opportunities and challenges in modern healthcare. This study aims to explore how AI can enhance the accuracy and efficiency of disease prediction, diagnosis, and treatment. Utilizing a systematic review approach, this research analyzes recent advancements in AI algorithms, including machine learning and deep learning, applied to epidemiological data. The findings reveal that AI significantly improves predictive accuracy and personalized medicine but also faces challenges related to data privacy, ethical concerns, and model interpretability. These challenges highlight the need for transparent algorithms and robust regulatory frameworks. This study contributes to the ongoing discourse on leveraging AI for public health, emphasizing the balance between technological innovation and ethical responsibility.
Modi et al. (Sat,) studied this question.