ChatGPT gives confident answers.
We give verified answers.
LLMs provide intelligence. RadAssistant adds the workflow layer: evidence tiers, UK-first guidance, and verification to reduce hallucinations.
Every statistic verified
Numbers are checked against retrieved source text; potentially unsupported claims are surfaced.
Evidence-tiered retrieval
Guidelines and high-quality studies are prioritized over low-level evidence. You see the tier.
UK-first for FRCR
NICE/RCR pathways are prioritized, and region-specific guidance can be flagged.
What you’ll see inside the app
- Confidence with a breakdown: relevance, evidence quality, citation coverage, and hallucination checks.
- Tier badges on sources (Guideline, Systematic, Review, RCT, Case).
- Next-step prompts when confidence is low (what to ask to increase certainty).
3-email “Trust in Radiology AI” mini-course
Use these as your conversion sequence. Keep them short, show a concrete failure mode, and link back here.
Email 1 (Day 0)
Subject: The statistic that wasn’t there
Most AI tools can fabricate plausible numbers and still cite a relevant paper. RadAssistant verifies statistics against the retrieved source text and surfaces potentially unsupported claims.
Email 2 (Day 2)
Subject: UK vs US guidance: the silent failure mode
Generic AI answers can be US-centric by default. RadAssistant prioritizes UK-first guidance for FRCR and can flag region-specific targets.
Email 3 (Day 5)
Subject: Case report ≠ guideline (and your AI won’t tell you)
A confident answer isn’t the same as high-quality evidence. RadAssistant tier-labels sources so you can see whether you’re reading a guideline, systematic review, or case report.