AI in Imaging: From Algorithm to Clinical Routine

I am at least 18 years old, have read the information above and agree to take part.

Experience

I have been involved in selecting, implementing or governing such tools

I have piloted, researched or developed them but do not use them routinely

I am familiar with such tools but have not used them myself

I have no experience with such tools so far

Insufficient technical performance of the algorithm (accuracy, robustness)

Unclear responsibilities: who decides and who is accountable in operation

Poor integration into IT systems and data flows (PACS/RIS, interfaces)

Poor fit with the clinical workflow, extra effort for users

No demonstrable economic value, lack of funding or reimbursement

Clear decision rights: who decides on selection and adoption

Common interfaces and standards across IT systems

Ability to scale tools to further sites and sustain them over time

Meeting regulatory duties throughout the tool's life cycle

Clear accountability during operation, including performance monitoring

High technical performance of the algorithm (accuracy, robustness)

Well-maintained imaging data that are comparable across sites

Trustworthiness: transparent results and defined human oversight

Central approval, with sites deciding on local use

Each site or department decides for itself

A central body in the network, such as an AI or quality board

Reading images themselves, largely as today

Clinical consulting and integrating imaging with other data

Being accountable for the quality and governance of AI and quantitative measurements

The profession will have largely disappeared

Automatically, with radiologist review only for abnormal or implausible values

Automatically and without radiologist review, provided the tool is validated