Annalise.ai parent company Harrison.ai has launched Harrison.rad.1, a radiology-specific vision language model.
Harrison.rad.1 is dialogue-based and can perform open-ended chats related to x-ray images, detect and localize radiological findings, generate reports, and provide longitudinal reasoning based on clinical history and patient context, Harrison.ai said.
In testing, Harrison.rad.1 performed on par with experienced radiologists on the Fellowship of the Royal College of Radiologists (FRCR) 2B Rapids exam and outperformed other models such as OpenAI's GPT-4o, Anthropic's Claude-3.5-sonnet, Google's Gemini-1.5 Pro, and Microsoft's LLaVA-Med, according to the company.
The model is now being made accessible to selected industry partners, healthcare professionals, and regulators around the world, the company added.
![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)
