Surveys reveal skepticism among older adults and patients toward healthcare AI, but clinicians show growing adoption and optimism, with key regulatory and rural access issues emerging.
Key Details
- 1More than half of American adults aged 50+ believe healthcare AI will do more harm than good; only 4% have strong trust in AI, while 49% have 'some' trust.
- 2Patients perceive physicians using AI as less competent and less trustworthy, per a University of Wuerzburg study published in JAMA Network Open.
- 3Clinician AI tool adoption doubled globally since last year (from 26% to 48%), highest in China (71%), lowest in US (36%) and UK (34%), per Elsevier’s report.
- 4Texas enacted the Texas Responsible Artificial Intelligence Governance Act, setting a major state-level precedent for AI regulation.
- 5Rural healthcare systems may be left behind in AI adoption due to lack of resources, unless aligned with larger health systems.
- 6Recent headlines cite specific imaging AI advances: deep learning for CT workflows, explainable AI for breast MRI, and new coverage guidelines for imaging AI.
Why It Matters
Understanding public and clinician trust, adoption disparities, regulatory moves, and access gaps are key to the successful integration of AI in healthcare and radiology. New imaging AI innovations and policy changes will directly impact clinical workflow, reimbursement, and patient care across regions.
Source
AI in Healthcare
Related News
•Radiology Business
Stanford and Rad Partners Propose Framework for Evaluating Radiology AI Models
Stanford and Rad Partners developed a structured framework for pre-deployment evaluation of radiology AI models to guide purchasing decisions.
•Cardiovascular Business
Radiology Leads in FDA-Cleared Imaging AI Algorithms, Cardiology Surges
Radiology maintains its lead as the specialty with the most FDA-cleared AI algorithms, while cardiology now surpasses 200 cleared tools.
•AuntMinnie
Deep Learning CT Muscle Segmentation Enhances Sarcopenia Detection in COPD
A deep learning model for CT-based 3D pectoralis muscle segmentation outperforms standard 2D techniques in assessing muscle loss among COPD patients.