Recent discussions highlight that AI in drug discovery must shift from merely modeling existing data to generating substantial new datasets, as current applications often suffer from insufficient experimental data to drive true innovation. While practitioners acknowledge that AI tools significantly accelerate routine tasks like structural modeling and data analysis, they have yet to yield genuinely novel scientific breakthroughs in the lab. Nevertheless, beyond traditional drug development, AI is already delivering tangible patient benefits by optimizing personalized care protocols and streamlining clinical trial management.