Artificial intelligence has become a vital tool in medicine, where it is used to analyze medical images such as X-rays with accuracy that exceeds human capabilities. For example, the CheXNet system demonstrated high efficiency in diagnosing pneumonia (Rajpurkar et al.). AI also assists in designing personalized treatments based on patients' genetic and health data, as implemented in systems like IBM Watson Health.

In big data analysis, AI enables tracking disease spread and predicting outbreaks, according to reports from the World Health Organization. Additionally, it supports doctors in diagnosis, with research by Esteva et al. showing that AI can diagnose skin cancer with accuracy comparable to dermatologists.

However, AI faces challenges such as reliance on data quality, lack of transparency, and high implementation costs. To address these issues, improving data quality, developing interpretable systems, and training doctors on AI tools are necessary.