The U.K. Royal College of Radiologists (RCR) has published fresh advice on how to integrate artificial intelligence (AI) image analysis software within the radiology workflow during the COVID-19 pandemic.
The RCR said there are five standards:
- AI must be integrated in the reporting (RIS and PACS) workflow in such a way that it does not add extra burden to radiologists.
- The accuracy (sensitivity and specificity) of the AI algorithm must be clearly displayed for radiologists and others making decisions on patient management.
- AI findings must be communicated to RIS using existing, widely used, global technical standards (HL7).
- AI findings must be communicated to PACS using existing, widely used, global technical standards (DICOM).
- The workflow must be robust enough to ensure AI analysis must be complete and available on PACS before a human reporter starts image interpretation.
The guidance, which was posted on 8 April, also discusses issues related to AI-assisted image interpretation for radiologists, AI assisting emergency doctors, mitigating risks associated with AI adoption, and technology components and interoperability requirements.
"AI image preanalysis is likely to have a very positive impact on radiologists' future working lives if properly integrated into the reporting workflow," the RCR wrote.
![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.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)
![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.auntminnieeurope.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)
