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
AI-Based Slab Reconstruction Streamlines Digital Breast Tomosynthesis
AI-driven slab reconstruction in DBT improves workflow efficiency without compromising diagnostic accuracy in breast cancer screening.
•AuntMinnie
AI Model Predicts Dosimetry for Lu-177 PSMA Therapy Using PET/CT
A machine learning PET/CT model shows promise for predicting radiation dose prior to Lu-177 PSMA therapy in prostate cancer patients.
•AuntMinnie
AI Model Uses Ultrasound to Assess Fetal Lung Maturity
Researchers demonstrated an AI model's strong accuracy in measuring fetal lung maturity from ultrasound images.