As the adoption of AI systems within the clinical setup grows, limitations in
bandwidth could create communication bottlenecks when streaming imaging data,
leading to delays in patient diagnosis and treatment. As such, healthcare
providers and AI vendors will require greater computational infrastructure,
therefore dramatically increasing costs. To that end, we developed intelligent
streaming, a state-of-the-art framework to enable accelerated, cost-effective,
bandwidth-optimized, and computationally efficient AI inference for clinical
decision making at scale. For classification, intelligent streaming reduced the
data transmission by 99.01% and decoding time by 98.58%, while increasing
throughput by 27.43x. For segmentation, our framework reduced data transmission
by 90.32%, decoding time by 90.26%, while increasing throughput by 4.20x. Our
work demonstrates that intelligent streaming results in faster turnaround
times, and reduced overall cost of data and transmission, without negatively
impacting clinical decision making using AI systems.

本文介绍了一种名为 “智能流媒体” 的新型框架，可用于加速、节约成本、优化带宽、提高 AI 推理在医学决策中的效率。通过在分类和分割方面取得了显著的优化，这一框架提供更快的周转时间、更少的数据传输成本以及更快的决策不良结果。