The author shares hands-on experience with Jev, exploring how it could make classification, filtering, cleaning, and workflow routing of unstructured healthcare data cheaper and faster.
I've been playing with Jev for the past few days. In healthcare, think about how much classification, filtering, cleaning there is on top of unstructured data. And how many workflows, how much routing there is. Now imagine if that becomes a lot cheaper and a lot faster...
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.
