A radiology resident built JevRad (educational use only) using TypeSafe's Jev model. After describing a patient, Jev picks the ACR scenario in 0.5s and selects the most appropriate imaging studies.
Radiology resident here. I've been experimenting with Jev from @typesafeai and built JevRad, for educational use only. Describe a patient → Jev picks the ACR scenario in 0.5s, then selects the most appropriate imaging studies. https://jevrad-web-production.up.railway.app cc @CompleteSkeptic
During live clinical consultations, an ambient scribe periodically feeds transcripts to Jev, which traverses a medical ontology to classify symptoms, update the differential diagnosis, suggest treatments, and flag red flags in real time.
The author demonstrates how to triage a patient to the right doctor using only TypeSafe AI's Jev model and some question-asking code, noting that no LLM is used during the conversation.
A developer shares building a calorie tracking app with Jev, highlighting its instant experience.
