Survey details:

• Target group: Radiologists and radiographers

• Time to complete: 10-12 minutes

• Survey link:  https://1ka.arnes.si/AI-Radiology

   

SUMMARY OF PROJECT:

Artificial intelligence (AI) tools are becoming more common in radiology. These
tools can help with tasks such as sorting urgent cases, assisting with image
interpretation, creating structured reports, or checking the quality of images. However, not all AI features are equally helpful in everyday clinical work, and we currently do not know which features radiology professionals find most valuable or most acceptable in practice.

The purpose of this study is to understand how members of the radiology workforce, such as radiologists, radiographers, and other imaging professionals, view different features that AI tools can offer. By collecting feedback directly from people who work with imaging every day, the research team hopes to learn which AI functions support clinical work effectively and which may be less useful or less desirable.

This study is being carried out because AI continues to grow rapidly in healthcare, yet many AI systems are designed without enough input from the professionals who actually use them. Understanding the needs and preferences of the radiology workforce is essential for designing safer, more practical, and more user-friendly AI tools in the future.

Therefore, the study will aim to answer questions such as:

• Which AI features do radiology professionals consider most helpful in their
  daily work?
• Which features are viewed as less useful, unnecessary, or potentially
  problematic?
• Do opinions about AI features differ depending on a person’s role, years of
  experience, or type of clinical setting?

What expectations or concerns do radiology professionals have about AI tools
used in practice?

• By analysing the responses, the research team expects to identify:
• A clearer picture of which AI features are most valued by the radiology
  workforce.
• Differences in attitudes or needs across professional groups or experience levels.
• Practical insights that can help guide the development of future AI tools so they better match the needs of real clinical environments.

Ultimately, the expected outcome is to support the creation of AI tools that are more meaningful, acceptable, and clinically useful for those who work in radiology.