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
Toronto Study: LLMs Must Cite Sources for Radiology Decision Support
University of Toronto researchers found that large language models (LLMs) such as DeepSeek V3 and GPT-4o offer promising support for radiology decision-making in pancreatic cancer when their recommendations cite guideline sources.
•AuntMinnie
AI Model Using Mammograms Enhances Five-Year Breast Cancer Risk Assessment
A new image-only AI model more accurately predicts five-year breast cancer risk than breast density alone, according to multinational research presented at RSNA 2025.
•Radiology Business
AI Model Uses CT Scans to Reveal Biomarker for Chronic Stress
Researchers developed an AI model to measure chronic stress using adrenal gland volume on routine CT scans.