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

TL;DR

This paper introduces a modular, data-centric platform designed to empower radiologists to lead AI innovation in medical imaging, reducing resource barriers and streamlining AI development and deployment.

Contribution

It presents a novel orchestration system that enables radiologists to actively participate in AI research without extensive technical resources or expertise.

Findings

Reduces resource and staffing barriers for radiologists in AI research.

Provides a user-friendly interface tailored for clinicians.

Streamlines AI development and deployment processes.

Abstract

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.