Even though we work on typeset all the time, sometimes it may seem that not much is happening. Austrian radiologists express positive attitudes towards ai in diagnostics, with 75.4% having basic knowledge and 84.4% willing to use certified ai software, but uncertainties remain about ai's future performance and potential to replace human radiologists. Future work will focus on integrating user feedback to dynamically adjust prompts and exploring multimodal ai approaches for enhanced visual and textual recipe generation.
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We address this gap by introducing a systematic methodology and a toolset to benchmark ai accelerators and their host devices with inference workloads representing mock convolutional neural network (cnn) models with varying input sizes, network sizes, layer types, and kernel sizes.
