Back to all news
Literature Review Highlights Gaps in Economic Evaluation of Healthcare AI
Tags:Research
A Finnish review finds significant gaps in economic evaluation reporting of AI technologies in Western healthcare.
Key Details
- 1A literature review examined economic evaluations of AI in healthcare, revealing insufficient research and inconsistent reporting.
- 2Over half of the reviewed studies (55.6%, n=10) only broadly described methods as 'ML' or 'deep learning,' lacking system-specific details.
- 3Most studies did not account for all costs, such as integration, support, or maintenance of AI systems.
- 4There is a need for unified guidelines for economic evaluation and reporting of AI in healthcare.
- 5The article emphasizes ongoing interdependencies and evolving performance of AI systems in real-world clinical settings.
Why It Matters
Clear and comprehensive economic evaluations are essential for decision-makers considering the adoption of AI in radiology and wider healthcare. The lack of robust, standardized evaluation methods may impede informed investments and implementation of AI technologies.
Source
AI in Healthcare
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.