Imaging that
actually fits a clinic's day.
Multi-modal, multi-location, multi-radiologist. The most operationally complex specialty we serve, and the one we've built deepest for.
Built for the shape of an
imaging operation.
Imaging is rarely "book an appointment". It's "which modality on which machine with what contrast under whose referral, and does the radiologist on shift cover it." We've modelled every one of those variables, and the front-line agents are tuned for clinical-imaging vocabulary, not generic medical NLP.
Built for the way imaging
groups actually work.
Referring-GP escalation
A doctor calling for a radiologist is not the same as a patient asking about Saturday hours. Triagents routes them differently: every time.
Modality-aware booking
MRI cervical spine, CT chest, ultrasound abdomen, each with its own prep, contrast, and clinician requirements. TriageChat captures the right modality on the first turn.
Specialist rostering
TriageWorkflow surfaces which radiologist is on, which modality is covered, and which slots are bookable, in real time, across every clinic in the group.
Prep & confirmation SMS
Booking confirmations include modality-specific prep (fasting, contrast, medications to hold): every time, without reception having to remember.
Result-call triage
When patients call back about a result, Triagents recognises the intent and routes the conversation straight to the right radiologist or registrar.
After-hours coverage
Urgent imaging requests after hours are escalated to the on-call radiologist immediately, with full context already captured.
What imaging groups ask first.
Does radiology AI booking make any clinical decisions?+
How does the agent know which modality and machine to book?+
What happens when a referring GP rings instead of a patient?+
Does it know which radiologist is on shift?+
Do patients get the right preparation instructions?+
From the blog
What we are learning, written down.
- What can AI agents actually do in radiology?Not AI reading scans. What an AI agent actually does across reception, booking, referrals, capacity and follow-up, and what it must never touch.
- We called 100 radiology practices after hours. Here is what answered.Original research: the same 100 radiology practices, rung on Sunday evening. Not one reached a person, and eleven were answered by an AI agent.
- We called 100 radiology practices. Here is what we found.Original research: we rang 100 Australian radiology practices and timed every call. Eight reached a person straight away; the median wait was 90 seconds.
Book a radiology network assessment.
We'll map it to your network.
30 minutes with a solutions engineer. We model your call volume, centre count, and integrations, then show the AI agents running on your own scenarios, and answer everything compliance- and security-related upfront.