Physician-founded · Radiologist-led
iTech Clinical Intelligence builds clinician-verified intelligence that connects imaging findings, clinical standards, enterprise systems and AI, from the radiologist's workstation to the developer API.
Built for clinicians. Available to platforms. Designed to integrate.
Architecture
General-purpose AI can generate extraordinary language. Clinical intelligence also requires maintained knowledge, deterministic rules, clinical context, provenance, quality control and a qualified professional at the end of the chain.
Dictated findings, report text, images or structured fields.
Free text becomes canonical findings: size, composition, laterality, with honest confidence.
Age, risk, screening status and priors that change which standard applies.
A validated criteria pack decides the category. Same input, same answer.
A language model writes and interprets, inside the verdicts it was given.
Mechanical checks: laterality conflicts, recommendation concordance, and prose that strays outside the verdicts.
Criteria pack, version, validation status and trace id travel with the answer.
A qualified professional reviews, edits and signs. The final authority stays human.
iTech builds the architecture around the model.
Three ways in
Use it
Clinical intelligence at the radiologist's workstation.
Embed it
Deterministic radiology inference and provenance your software can call.
Extend it
An intelligence layer designed to connect across imaging infrastructure.
Ecosystem
The same clinical engine reaches radiologists through RadVector Reporting and software, AI vendors and agents through RadVector API. Connections are labeled by what exists today.
Select a surface to see what exists today.
Radiologist via RadVector Reporting Live: In production in a shipping product today.
Radiologists use the intelligence layer inside RadVector Reporting: report construction from their own findings, criteria, QA, recommendations and references, with the radiologist signing every report.
EHR / RIS Integration-ready: Built and available for integration projects.
RIS REST and FHIR R4 clients read worklists, and an HMAC-signed HL7 v2 listener accepts orders (ORM), for integration projects. A browser connector links RIS worklists today.
AI agent Available via API: Callable today through RadVector API.
An AI agent can call RadVector API as a tool today: a finding goes in, a versioned verdict with provenance comes out, and a deferral says why when the criteria do not apply.
PACS / VNA Coming soon: In development. Not yet available.
PACS and DICOMweb connectivity is in development and not yet available. The design goal is to add intelligence alongside the viewer and archive, not replace them.
Modality Direction: Architectural direction. Not a current product capability.
Architectural direction: combining acquisition context with criteria and clinical history downstream. No modality integration exists today.
AI vendor Available via API: Callable today through RadVector API.
Imaging-AI vendors can pass a detection to RadVector API and receive the criteria-based category and recommendation that follows from it.
Software Available via API: Callable today through RadVector API.
Reporting, PACS, RIS and EHR software can call the same deterministic engine that powers RadVector Reporting, over a versioned REST API.
Research Available via API: Callable today through RadVector API.
Research teams can apply versioned, validated criteria consistently across de-identified datasets, with a trace id on every verdict.
RadVector API
RadVector separates deterministic clinical decisions from generative language, and returns the criteria, version, provenance and uncertainty behind every result.
curl https://api.radvector.ai/v1/inference/evaluate \
-H "Authorization: Bearer $RADVECTOR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"study": {
"modality": "ct",
"region": "thorax",
"age_years": 64,
"sex": "M",
"attributes": {
"risk": "high"
}
},
"findings": [
{
"label": "nodule",
"region": "thorax",
"laterality": "right",
"composition": "solid",
"measurement": {
"value_mm": 7
},
"attributes": {
"multiplicity": "single"
}
}
]
}'import os, requests
resp = requests.post(
"https://api.radvector.ai/v1/inference/evaluate",
headers={"Authorization": f"Bearer {os.environ['RADVECTOR_API_KEY']}"},
json={
"study": {
"modality": "ct",
"region": "thorax",
"age_years": 64,
"sex": "M",
"attributes": {
"risk": "high"
}
},
"findings": [
{
"label": "nodule",
"region": "thorax",
"laterality": "right",
"composition": "solid",
"measurement": {
"value_mm": 7
},
"attributes": {
"multiplicity": "single"
}
}
]
},
)
best = resp.json()["outcomes"][0]["best"]
print(best["impression_category"], best["provenance"]["pack_version"])const resp = await fetch("https://api.radvector.ai/v1/inference/evaluate", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.RADVECTOR_API_KEY}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"study": {
"modality": "ct",
"region": "thorax",
"age_years": 64,
"sex": "M",
"attributes": {
"risk": "high"
}
},
"findings": [
{
"label": "nodule",
"region": "thorax",
"laterality": "right",
"composition": "solid",
"measurement": {
"value_mm": 7
},
"attributes": {
"multiplicity": "single"
}
}
]
}),
});
const { outcomes } = await resp.json();
console.log(outcomes[0].best.recommendation);{
"trace_id": "site-example-1",
"api_version": "v1",
"outcomes": [
{
"best": {
"schema_version": "1.0",
"system": "Fleischner",
"category_id": "FLE-SOLID-SINGLE-6TO8-HIGH",
"impression_category": "Solid single nodule 6-8 mm, high risk",
"canonical_language": "Single solid pulmonary nodule measuring 6 to 8 mm in a high-risk patient.",
"recommendation": "CT at 6-12 months, then CT at 18-24 months.",
"confidence": 0.85,
"authoritative": true,
"usable_for_output": true,
"is_deferral": false,
"provenance": {
"system": "Fleischner",
"pack_version": "fleischner-2017-r2",
"validation_status": "validated",
"validated_by": "Owner-directed (founding radiologist); size/risk table audited vs Fleischner 2017 (Radiology 2017;284:228-243)",
"validation_date": "2026-08-18",
"knowledge_release_id": "",
"engine": "rc_algorithmic",
"trace_id": "site-example-1",
"citations": [],
"is_clinically_validated": true
},
"evidence": {
"type": "solid",
"multiplicity": "single",
"size_mm": 7,
"risk": "high"
},
"notes": ""
},
"systems_considered": [
"Fleischner"
],
"engine_input": "CT of the chest. 64-year-old male. Patient is high-risk. Single solid pulmonary nodule on the right measuring 7 mm.",
"engine": "rc_algorithmic"
}
]
}Captured from the RadVector API engine. verdicts[] and authoritative repeat this verdict and are omitted here. The contract has no field for a name, MRN or date of birth.
Beyond generation
A model turns pixels and words into representations. Turning a representation into a clinical decision takes criteria, a version, evidence and a record of how the answer was reached.
Domain intelligence
Clinical decisions should expose the criteria, version, evidence and reasoning context that produced them. Not because AI should be trusted blindly. Because it shouldn't have to be.
Available via API: Callable today through RadVector API. Versioned, validated criteria packs answer through the API today.
Coming soon: In development. Not yet available. A maintained corpus of published guidance and newer evidence is being wired in.
Decision
Input: 7 mm solid nodule, CT thorax, risk high
RecommendationCT at 6-12 months, then CT at 18-24 months.
From a real RadVector API response. Clinical decision support for qualified professionals.
Validated · may drive output
Every verdict names who validated its pack and when.
Draft · answers as advisory
Draft packs are marked not usable for output. A deferral always says why.
Model orchestration
Clinical AI should not depend on a single model vendor. iTech routes different workloads across multiple commercial and open-weight models, while clinical governance stays above the model layer.
Construction. Builds the report through FINDINGS from the radiologist's own words. Findings authority is enforced mechanically downstream.
Reasoning. Drafts IMPRESSION and RECOMMENDATIONS around the deterministic verdicts instead of recalling a guideline from memory.
QA. Can run on a different model than the one that drafted, so a second system checks the first.
Vision. Reads worksheets, tech sheets and images, behind an OCR-based PHI gate.
References. Checks cited claims against a registry of published sources and shows what was consulted.
Every report records the model that actually ran, with its tokens and cost. Organizations choose their vendors and models, including their own keys on enterprise plans.
Enterprise imaging
PACS, VNA, RIS, EHR, modalities, AI models and reporting systems each hold part of the imaging workflow. iTech is building the intelligence layer that connects across them, without asking anyone to converge on one vendor.
RadVector Reporting
RadVector Reporting builds the report through FINDINGS from the radiologist's own words, then drafts the interpretation around them. The interpretive half of the report cannot rewrite what the radiologist observed, and the radiologist signs every report.
QLumis
QLumis extends iTech's work into persistent memory, identity, multimodal interaction and long-term human-AI continuity, with consent built into voice and likeness.
QLumis is a consumer AI companion. It is not a medical device and is separate from RadVector.
Physician-founded · Radiologist-led
iTech began with problems encountered in day-to-day radiology. What started as decision-support software became a broader architecture for reporting, clinical inference, model orchestration and human-AI interaction.
Founded by a practicing, board-certified radiologist with graduate training in computer science, data science and artificial intelligence.
Software drafts, checks and cites. A qualified professional decides and signs.
Every verdict carries the criteria, version and validation status that produced it.
Models, vendors and imaging systems change. The architecture should not depend on any one of them.
Trust
Trust should come from specifics you can check, not from badges.
Decision support for qualified professionals. Not autonomous diagnosis.
No field for a name, MRN or date of birth. Free text is masked or refused.
Pack, version, validation status and trace id on every verdict.
Each report records which model actually ran, with tokens and cost.
Let's connect the intelligence.