Survey finds over 75% of radiology organizations using AI lack clear, quantified ROI data.
Key Details
- 1Black Book Research surveyed over 200 hospital and clinic leaders ahead of RSNA.
- 2More than 75% of organizations using radiology AI do not have quantifiable ROI information.
- 3About 24% had measurable ROI data; of these, 36% received AI payments per study.
- 4Other payment structures included bundled payments (25%), enterprise licenses (20%), and per-user pricing (10%).
- 5Despite lack of ROI clarity, most leaders reported positive or neutral feelings about AI's impact.
Why It Matters
As AI becomes more integrated into radiology workflows, the lack of clear ROI data could slow adoption or influence future investment decisions. Understanding payment models and satisfaction levels helps stakeholders adapt strategies for successful AI implementation.
Source
Radiology Business
Related News
•AuntMinnie
Study: Computer Vision Models Best LLMs in Chest CT Breast Abnormality Detection
Computer vision models (CVMs) surpass large language models (LLMs) in accurately labeling incidental breast abnormalities on chest CT scans.
•AuntMinnie
Deep Learning Models Rival Radiologists for Pancreatic Cancer Detection on CT
Deep-learning models achieved comparable or superior accuracy to experienced radiologists in detecting pancreatic cancer on CT scans, especially for small tumors.
•Radiology Business
Radiology AI Devices at Elevated Risk for FDA Recalls, Study Finds
Radiology AI devices are more likely to face FDA recalls, largely due to deviations from intended use and incomplete clinical data.