The Mission
RAD-Flow is a vendor-independent software concept that supports hospitals in the adoption of radiology AI. Instead of creating yet another diagnostic algorithm, RAD-Flow focuses on a different challenge: understanding whether AI tools actually create value in real clinical workflows. The project aims to help healthcare providers evaluate the expected and observed impact of radiology AI on reporting time, workflow efficiency, investment decisions, and transparency of use. In the long term, RAD-Flow could evolve into a lightweight platform for workflow simulation, post-deployment monitoring, and governance support, helping hospitals adopt AI in a more evidence-based, transparent, and responsible way.
The Challenge
Radiology AI is developing rapidly, and hospitals are increasingly exposed to new tools from different vendors. However, adopting AI is not only a technical question of model accuracy. Hospitals also need to know whether these tools fit their workflow, reduce workload, justify their costs, and can be implemented transparently and responsibly. In many cases, decision-makers must rely heavily on vendor claims, fragmented information, and incomplete local evaluation. This creates uncertainty around effectiveness, return on investment, and governance of AI in practice. There is therefore a need for a simple and independent way to evaluate the real-world value and responsible adoption of radiology AI.
The solution
RAD-Flow is a lightweight, vendor-independent software concept designed to help hospitals evaluate the real-world operational impact of AI in radiology. The platform aims to support two stages of AI adoption: before implementation, hospitals could simulate how a new AI solution might affect reporting times, workloads, and costs. After implementation, RAD-Flow could analyze available workflow data, such as examination timestamps and AI processing records, to identify delays, missing results, and differences between expected and observed performance. Rather than developing another diagnostic AI, RAD-Flow focuses on evaluating how existing AI technologies function within hospital workflows. Its goal is to provide transparent, evidence-based insights that support better investment decisions, operational efficiency, and responsible AI adoption. The initial prototype will focus on workflow simulation and analysis of synthetic operational data, with future development guided by research and feedback from healthcare professionals.