Radiologists often work under intense pressure. They need to interpret complex images fast and document their findings accurately. Traditional methods—typing or dictating to a transcriptionist—slow things down and open the door to error.

Opportunity

We saw an opportunity to lighten the load for radiologists b designing a voice-enabled AI tool, we aimed to simplify documentation, reduce error, and free radiologists to focus on diagnosis—not admin. The product would need to handle complex medical language with high accuracy, fit seamlessly into existing workflows, and unlock structured data for future automation.

Design

We started where all good design does: with people. Through interviews and Radiologist workflow shadowing, we uncovered the friction points. We built and tested low-fi prototypes, refining features around real usage. We focused on enhancing—not disrupting—the hanging protocol radiologists already use.

Speech recognition and NLP were layered in carefully. By starting with single-body-part workflows, we trained the AI to match human-level accuracy. Contextual cues, customisation, and robust error handling helped reduce friction and build trust.

Features

• Voice-first interface: Dictation auto-structured into intuitive, report-ready formats

• Contextual transcription: Understands medical terminology and radiology-specific language

• Faster reviews: Patient intelligence and prompts to speed up decision-making

• Automated documentation: Captures findings, images, and recommendations in real time

• Privacy built-in: Designed to meet strict healthcare compliance standards

Impact

• 40% faster documentation time

• 30% increase in user satisfaction

• Structured data enabled real-time business insights