The American College of Radiology (ACR) has released a draft practice parameters document on AI in radiology and has opened the comment period for its 2026 Practice Parameters and Technical Standards.
The document is designed to provide radiologists, healthcare systems, and industry partners with a national framework for integrating AI into clinical practice. According to the ACR, it addresses the following five key issues:
- Governance and accountability for AI in imaging workflows
- Clinical validation and performance monitoring in real-world use
- Bias mitigation and transparency in algorithm design and deployment
- Integration strategies to improve efficiency without compromising quality
- Ongoing quality assurance over time
ACR members and Society of Imaging Informatics in Medicine (SIIM) members via staff are strongly encouraged to review the draft and provide feedback by September 26, 2025.
Find more information on the ACR website.
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
