Imagine an AI that can analyze X-rays and write up the findings in Japanese. That's precisely what researchers at the University of Tokyo Hospital have developed with JRadiEvo. This innovative system tackles the critical challenge of applying advanced AI models in healthcare settings where English isn't the primary language. It's also designed to address patient privacy, a major concern when handling sensitive medical data. Traditional methods of training AI models for medical tasks require massive amounts of data, which can be difficult and expensive to acquire, especially in languages other than English. JRadiEvo sidesteps this problem with a clever technique called evolutionary model merging. Instead of learning from scratch, it combines the strengths of several existing AI models, each specialized in different areas, like image analysis, medical text generation, and Japanese language. This process allows JRadiEvo to generate accurate Japanese radiology reports using a surprisingly small training dataset of just 50 translated samples. In tests, JRadiEvo performed remarkably well, even outperforming larger, more complex models. This lean, efficient approach allows for local deployment within hospitals, minimizing reliance on external cloud services and keeping patient data secure. While still in its early stages, JRadiEvo showcases the power of innovative AI techniques to overcome challenges in global healthcare, especially in non-English speaking regions. It promises faster, more efficient analysis of medical images, ultimately benefiting both doctors and patients. Future research will focus on fine-tuning JRadiEvo's accuracy and gaining expert validation to pave the way for real-world clinical applications.