Rad AI has introduced its next-generation speech recognition technology, designed to improve the speed and accuracy of diagnostic reporting of its Rad AI Reporting system.
The company noted that its next-generation radiology report dictation system understands clinical context, recognizes uniqueness, and adapts to individual radiologists.
The system's multimodal architecture is designed to deal accurately with diverse accents, overlapping speech, and the complexity of medical terminology. Fine-tuned language models handle radiology-specific terminology, measurements, modifiers, and content-based interpretation cues such as laterality and sequence timing.
New key features include a proprietary algorithm which dynamically compares multiple transcriptions simultaneously to select the most accurate output, custom fine-tuned language models, real-time analytics, and sub-second latency-free transcription within the existing Rad AI Reporting interface.


















![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&fit=crop&h=112&q=70&w=112)
