This study evaluates the capabilities of artificial intelligence (AI) in scientific research, focusing on the prediction of breast cancer recurrence using MRI data. Here, ChatGPT 4o was employed to propose complementary analytical approaches, demonstrating potential in generating valid statistical methods such as PCA, K-means, Random Forest, and Cox models. However, significant limitations were observed in its ability to perform numerical calculations accurately. For instance, discrepancies in silhouette coefficients and variable importance rankings highlighted the AI’s unreliability in executing complex analyses without supervision. While ChatGPT 4o proved useful for hypothesis generation and experimental design, its lack of transparency and consistency underscores the need for human oversight. These findings suggest that AI can improve scientific research but is not yet ready to replace human expertise in rigorous, unsupervised analysis. The study calls for a balanced integration of AI tools, emphasizing ethical and methodological considerations in the evolving landscape of automated research.