Transforming Radiology Through AI Insights

Explore trust, concerns, and adoption of AI in radiology through our comprehensive research.

Exploring AI in Radiology Together

At Radai View, we conduct in-depth qualitative research and surveys to enhance AI integration in radiology, focusing on trust, explainability, and policy recommendations for better healthcare outcomes.

AI Research Services

We conduct qualitative exploration and surveys to enhance AI integration in radiology.

A person in a medical setting is lying on a table, about to enter a large cylindrical medical imaging machine labeled 'Radixact'. Another person, wearing a white coat, stands beside the machine, using a touchscreen panel.

Qualitative Exploration Phase

In-depth interviews with radiology professionals to understand AI experiences and needs.

Survey Design Phase

Crafting comprehensive surveys to assess trust, concerns, and policy recommendations in AI.

Thematic Analysis Framework

Utilizing GPT-4 for automated thematic coding and summarization of research findings.

Radiology Insights

Exploring AI in radiology through qualitative and quantitative research.

Two people in lab coats collaborate in an office setting. One is seated and pointing at a computer screen displaying medical images, while the other stands nearby holding a tablet. Large windows reveal a view of greenery outside, creating a bright and professional environment.

Phase One

Qualitative exploration of AI in radiology practices.

A hospital room containing an MRI machine surrounded by overhead lights and various medical equipment. The room has a clean and clinical environment with red cabinets on the left and diagnostic machines on the right.

Phase Two

Survey design for large-scale deployment of AI insights.

As AI technologies rapidly advance in medical imaging, the radiology community’s attitudes toward AI for diagnostic interpretation, report automation, and clinical decision support are evolving from cautious observation to active acceptance. However, existing literature largely focuses on algorithmic performance metrics, with scant systematic exploration of how radiologists, radiologic technologists, and imaging center managers perceive AI—specifically trust levels, concerns, willingness to use, and the mechanisms by which these attitudes affect clinical practice integration.

Our central research question is:

Trust & Explainability Needs: How much do radiologists trust AI-generated lesion annotations and diagnostic suggestions? What are their expectations and acceptance levels for AI chain-of-thought explainability outputs?

Concerns & Barriers: Which primary concerns (misdiagnosis risk, legal liability, workflow disruption, data privacy) pose barriers to AI utilization, and how do these differ across professional roles?

Adoption Intent & Use Cases: In real-world practice, under what scenarios are radiologists willing to employ AI assistance—initial screening, secondary review, report drafting, or training?

Influence Mechanism Modeling: Based on Technology Acceptance Model (TAM) and organizational behavior theories, how can we construct a multilevel path analysis to reveal causal links among attitudes, subjective norms, perceived usefulness, and actual use behaviors?