  As the adoption of Artificial Intelligence (AI) systems within the clinical
environment grows, limitations in bandwidth and compute can create
communication bottlenecks when streaming imaging data, leading to delays in
patient care and increased cost. As such, healthcare providers and AI vendors
will require greater computational infrastructure, therefore dramatically
increasing costs. To that end, we developed ISLE, an intelligent streaming
framework for high-throughput, compute- and bandwidth- optimized, and cost
effective AI inference for clinical decision making at scale. In our
experiments, ISLE on average reduced data transmission by 98.02% and decoding
time by 98.09%, while increasing throughput by 2,730%. We show that ISLE
results in faster turnaround times, and reduced overall cost of data,
transmission, and compute, without negatively impacting clinical decision
making using AI systems.
