# An Orchestration Platform that Puts Radiologists in the Driver's Seat of AI Innovation: A Methodological Approach

arXiv: https://arxiv.org/abs/2107.04409 · HTML: https://mlsanity.com/paper/2107.04409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 6 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Integrating AI into Radiology workflow: Levels of research, production, and feedback maturity](https://mlsanity.com/paper/1910.06424/) (2019)

## Built on by (most cited first, 2 of 2)

- [Advancing Brain Metastases Detection in T1-Weighted Contrast-Enhanced 3D MRI using Noisy Student-based Training](https://mlsanity.com/paper/2111.05959/) (2021)
- [A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Research](https://mlsanity.com/paper/2112.06883/) (2021)

## Abstract (arXiv)

Current AI-driven research in radiology requires resources and expertise that are often inaccessible to small and resource-limited labs. The clinicians who are able to participate in AI research are frequently well-funded, well-staffed, and either have significant experience with AI and computing, or have access to colleagues or facilities that do. Current imaging data is clinician-oriented and is not easily amenable to machine learning initiatives, resulting in inefficient, time consuming, and costly efforts that rely upon a crew of data engineers and machine learning scientists, and all too often preclude radiologists from driving AI research and innovation. We present the system and methodology we have developed to address infrastructure and platform needs, while reducing the staffing and resource barriers to entry. We emphasize a data-first and modular approach that streamlines the AI development and deployment process while providing efficient and familiar interfaces for radiologists, such that they can be the drivers of new AI innovations.