# ISLE: An Intelligent Streaming Framework for High-Throughput AI Inference in Medical Imaging

arXiv: https://arxiv.org/abs/2305.15617 · HTML: https://mlsanity.com/paper/2305.15617/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 6 of 6)

- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Machine Vision Guided 3D Medical Image Compression for Efficient Transmission and Accurate Segmentation in the Clouds](https://mlsanity.com/paper/1904.08487/) (2019)
- [Structured dataset documentation: a datasheet for CheXpert](https://mlsanity.com/paper/2105.03020/) (2021)

## Built on by (most cited first, 1 of 1)

- [Towards Resource-Efficient Streaming of Large-Scale Medical Image Datasets for Deep Learning](https://mlsanity.com/paper/2307.00438/) (2023)

## Abstract (arXiv)

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.