What should be the primary focus to improve an AI-driven diagnostic system's real-time processing capabilities when high-resolution images cause performance degradation?

The primary focus to improve an AI-driven diagnostic system's real-time processing capabilities when high-resolution images cause performance degradation should indeed be on optimizing the AI model's architecture for better parallel processing on GPUs.

This is crucial because modern AI systems often rely on Graphics Processing Units (GPUs) to handle large volumes of data and perform complex calculations simultaneously. By optimizing the architecture, you can enhance the model's efficiency in utilizing GPU resources, which is critical for real-time applications that require fast processing of high-resolution images. Improvements could involve techniques such as model pruning, quantization, or restructuring the neural network to better leverage the parallel processing capabilities of GPUs. This results in faster inference times while maintaining accuracy, allowing the system to handle higher resolutions without significant degradation in performance.

Focusing solely on increasing system memory or moving to a CPU-based system does not directly address the bottleneck caused by the model's capacity to process high-resolution data quickly. Lowering the resolution of input images, while it might reduce the processing load, can negatively impact the diagnostic system's ability to provide accurate and detailed analyses, which is counterproductive to the system's purpose.