We design and build AI screening platforms — imaging AI, pathology AI, and risk-stratification models that flag cancer risk earlier, built by one team.
Radiology and pathology teams are being asked to read more studies, catch more early-stage disease, and document more thoroughly — without a proportional increase in staff or hours in the day.
High-volume screening programs create worklist backlogs where every case waits the same amount of time, regardless of urgency.
Subtle findings — a small nodule, an early lesion — are easiest to miss during a busy, high-volume reading session.
Imaging findings, pathology results, and clinical risk factors live in separate systems, so no one sees the full risk picture at once.
FDA scrutiny of AI/ML-enabled screening software is increasing along with clearance volume — validation and monitoring now matter from day one.
A flagged finding with no reliable recall and follow-up workflow behind it is a missed diagnosis waiting to happen.
Promising detection models trained on research datasets rarely survive contact with real PACS data, real worklists, and real regulatory review.
An oncology & cancer screening AI platform is the software and AI layer that reads medical images — mammograms, CT scans, pathology slides — and clinical data, flags abnormalities, assigns a risk score, and routes the case to a clinician for confirmation.
It's built around imaging and clinical data, not lab-based molecular testing — the AI pre-reads and prioritizes, your radiologists and pathologists make the call, and the platform tracks every patient through to follow-up.
Our imaging and data pipelines are designed around the modality your program runs on — not a one-size-fits-all schema.
Breast cancer screening AI for full-field digital mammography and digital breast tomosynthesis.
Nodule detection, characterization, and malignancy likelihood scoring for lung cancer screening programs.
Digital pathology AI for tumor detection, grading, and quantification on scanned whole-slide images.
Real-time polyp and lesion detection support during colonoscopy and upper endoscopy procedures.
Image-based triage models for suspicious skin lesions ahead of dermatologist or teledermatology review.
AI-assisted cytology and HPV co-testing workflows for cervical screening programs.
Lesion detection and PI-RADS-aligned scoring support for prostate MRI screening and biopsy planning.
Risk-stratification models that flag elevated cancer risk from structured clinical and history data alone.
This is the process our platforms are built to run end-to-end, with the clinician kept in the loop at every decision point.
DICOM studies from PACS/RIS or structured clinical data are ingested automatically.
The AI model flags abnormalities and assigns a quantified risk score to each case.
The radiologist or pathologist reviews the AI-prioritized case and confirms or overrides it.
A structured report is generated with the finding, risk score, and recommended next step.
Flagged patients are routed into a recall and follow-up queue so nothing falls through.
Regulatory clearance volume, screening adoption, and clinical trust are all moving at once — the platforms built now shape who leads the category.
The global AI-in-oncology market is projected at roughly $2.87B in 2026, growing at a 33.9% CAGR through 2033.
As of March 2026 there are 1,524 FDA-cleared AI algorithms, with 68 new radiology clearances in Q1 2026 alone.
Newly cleared lung screening AI, like Median Technologies' eyonis LCS (cleared Feb 2026), is expanding CT screening capacity.
At least eight FDA-cleared products now span mammography, tomosynthesis, and breast MRI screening AI.
Whole-slide imaging AI is moving from pilot programs into standard-of-care pathology workflows at scale.
Health systems are scaling structured screening programs that need AI triage to handle rising volume.
Payers and population-health organizations are piloting AI-augmented screening to close early-detection gaps.
Oncology AI remains a well-funded category for imaging, pathology, and risk-stratification startups alike.
Building screening software means understanding DICOM, PACS worklists, and clinician workflows just as well as system architecture — that's the team we bring.
Our engineers work fluently with DICOM, PACS/RIS worklists, and whole-slide pathology formats — not just generic backend architecture.
We build with predicate device research, validation, and audit trails in mind from day one, not bolted on before a submission.
We take imaging and risk-stratification models from research notebooks to versioned, monitored, clinical-grade production services.
We've integrated PACS/VNA systems, RIS platforms, and EHRs into unified screening and reporting pipelines.
Review workstations designed with practicing clinicians in mind — a prioritized worklist, not a raw data dump.
We've taken screening platforms from first pilot site through thousands of studies a month without a rebuild.
Talk to our team about your modality, data volume, and regulatory pathway.
AI-augmented screening is moving from pilot to standard-of-care faster than almost any other digital health category right now.
Sources: market sizing and CAGR estimates aggregated from published 2025-2026 AI-in-oncology market research reports; FDA clearance figures from FDA AI/ML-enabled medical device list updates, March 2026.
These are real, publicly documented features from today's leading oncology imaging and pathology AI platforms. Our expert imaging and AI development team can build you a comparable feature — or extend it further for your specific screening program.
Assigns every mammogram a malignancy likelihood score and classifies lesions, with published studies showing up to a 15% lift in early-stage detection.
Per-image AI risk scoring integrated directly into your existing PACS viewer, tuned to your patient population.
AI-prioritized worklist management for chest X-ray that cut reading workload by 36.2% in published studies while holding 95% sensitivity for urgent cases.
Risk-ranked worklist triage that pushes your highest-priority studies to the top automatically.
A cloud-native digital pathology platform with intelligent case management, FDA 510(k)-cleared for primary diagnosis with major slide scanners.
Cloud-native slide management and case triage built around the scanners your pathology lab already runs.
The first FDA De Novo-authorized AI pathology product, built to help pathologists find small, easy-to-miss foci of prostate cancer.
Small-lesion detection models tuned to your tissue type, with a review workflow your pathologists will actually use.
Concurrent-read detection of malignant densities and calcifications on 3D tomosynthesis, plus FDA-cleared objective breast-density scoring.
Tomosynthesis detection models paired with automated density scoring, delivered as one integrated feature.
FDA-cleared for lung cancer screening, and the only device able to both detect and characterize lung nodules on low-dose CT (93.3% sensitivity, 92.4% specificity).
Nodule detection plus malignancy characterization and volume tracking, built for your CT lung screening program.
Feature descriptions above reflect each company's own published clinical and product data as of 2026 and are cited for reference only — Idea Usher is not affiliated with Lunit, PathAI, Paige, iCAD, Volpara, or Median Technologies. We build comparable capability for your platform from scratch, using our own models trained on your data.
From imaging ingestion through AI models to patient recall — the full stack a cancer screening program actually needs.
Automated ingestion of imaging studies from PACS and RIS systems into your AI pipeline.
Detection and classification models for mammography, CT, MRI, and other imaging modalities.
Digital pathology models for tumor detection, grading, and quantification on scanned slides.
Models that flag elevated cancer risk from structured clinical history and lab data.
Screening-focused models tuned for high specificity across multiple cancer types at once.
Radiologist and pathologist-facing workstations with side-by-side comparison and annotation tools.
Risk-based worklist ordering so the highest-priority cases reach a clinician first.
Clear, structured reports generated automatically from AI findings and clinician confirmation.
HL7/FHIR and DICOMweb-based integration so results flow directly into existing systems.
Workflows that route flagged patients into scheduling, outreach, and follow-up tracking.
Ongoing checks for model drift and performance disparities across patient subgroups.
Clinical validation studies and evidence packages to support regulatory submissions.
Guidance on predicate device strategy and validation-ready software architecture from the start.
Screening completion, recall compliance, and outcomes reporting across your entire program.
Ongoing support, model performance monitoring, and drift detection after your platform goes live.
The same platform, three purpose-built experiences — for the reading clinician, the care coordinator, and the compliance team.
These are the teams that typically need custom software around their imaging, pathology, and screening workflows.
A structured process built around how screening software actually gets validated and shipped.
We map your modality, data volume, and target regulatory pathway.
We design a HIPAA-aware, FDA SaMD-ready system architecture before writing code.
Ingestion, AI pipeline, review workstation, and reporting built in transparent sprints.
Detection and risk models built, trained, and validated against your data.
PACS, RIS, and EHR integrations tested end-to-end with real reading workflows.
Go-live support plus continuous model performance and bias monitoring.
We build the AI layer as production software — versioned, monitored, and validated against held-out data — and connect it to the imaging and clinical systems your reading clinicians already use.
A validation-minded process built for regulated screening software, not a generic app-dev checklist.
Modality, data volume, and regulatory target mapped alongside your reading workflow.
HIPAA-aware, FDA SaMD-ready architecture and data model design.
DICOM ingestion, de-identification, and clinical data pipeline development.
Detection, classification, and risk models trained, tested, and validated.
Clinician review UI and care-coordination dashboard built and tested.
PACS/RIS/EHR integration and clinical validation testing against real cases.
Submission-ready documentation, go-live support, and ongoing model monitoring.
We adapt this stack to your existing PACS, RIS, and EHR systems — nothing here is a forced migration.
Cancer screening AI software fails quietly if it's built by a team that hasn't shipped regulated imaging platforms before.
AI-based screening software carries real regulatory weight — we build with validation and audit-readiness as first-class requirements, not an afterthought.
Architecture and documentation built with the FDA's Software as a Medical Device framework in mind.
We help map your model against existing cleared predicates to support a substantial-equivalence submission.
Post-market performance monitoring aligned with the FDA's AI/ML action plan for continuously learning systems.
Electronic records and signature practices modeled on 21 CFR Part 11 expectations for regulated data.
Encryption at rest and in transit, minimum-necessary access, and BAAs across every vendor in the stack.
Protected health information stripped from imaging metadata before it enters training or analytics pipelines.
Every study, AI finding, and clinician override logged and traceable for inspection readiness.
Data residency and consent controls available for screening programs operating outside the US.
We are a software development partner, not a regulatory law firm or CRO — we build the technical controls and documentation scaffolding your regulatory and clinical teams need, and always recommend pairing us with qualified regulatory counsel.
We help you architect the platform around the commercial model you're actually pursuing.
License the platform to radiology and pathology groups running their own reading workflows.
Deploy across hospital networks as an enterprise platform for population-scale screening programs.
Support payer-sponsored screening initiatives with data-sharing and outcomes-reporting agreements.
Offer the platform white-labeled to imaging AI companies who want to launch under their own brand.
Representative engagements across radiology groups, digital pathology companies, and screening programs.
Built an AI-prioritized worklist for a multi-site radiology group's lung screening program from zero to first live site in under 6 months.
Took a research-stage tumor-detection model into a versioned, monitored production service integrated with the client's slide-scanning workflow.
Replaced a spreadsheet-based recall list with a structured dashboard tracking every flagged patient to a follow-up appointment.
"Idea Usher understood DICOM and PACS worklists from the first call — we didn't have to educate them on radiology workflow before we could talk about software."
"They took our research model and turned it into a monitored production service without losing a point of accuracy. That's a rare skill set."
"Our recall completion rate finally moved because patients stopped falling through the cracks between a flagged scan and a scheduled follow-up."
Tell us about your modality, data volume, and regulatory pathway — we'll map what an MVP and full platform would look like.
It typically includes an imaging or clinical-data ingestion pipeline (DICOM from PACS/RIS, or structured EHR data), one or more AI models that flag abnormalities and assign a risk score, a radiologist or pathologist review workstation with an AI-prioritized worklist, a structured reporting layer, and a care-coordination dashboard for patient recall and follow-up.
A focused MVP covering one imaging modality, a single AI model, and a basic review workstation typically takes 5-7 months. A full platform with multiple AI models, PACS/RIS/EHR integrations, a care-coordination dashboard, and FDA 510(k)-ready validation documentation usually takes 10-16 months, depending on how many modalities and models are in scope.
Both. We build the imaging and risk-stratification AI models themselves as well as the platform around them — ingestion pipelines, review workstations, and reporting. If you already have a model you've trained or licensed, we can integrate it into a production-grade platform instead.
Yes. We've built integrations with PACS and VNA systems over DICOMweb, and with EHR platforms like Epic and Cerner/Oracle Health over HL7/FHIR. We map into your existing imaging and clinical workflows rather than asking you to replace systems.
We build with FDA Software as a Medical Device (SaMD) and 510(k) pathway considerations in mind from day one — predicate device research, validation documentation, audit trails, and post-market performance and bias monitoring aligned with the FDA's AI/ML action plan. We are a software development partner, not a regulatory law firm or CRO, and always recommend pairing us with qualified regulatory counsel.
Most engagements range from $100,000 for a focused single-modality MVP to $500,000+ for a full multi-model platform with deep PACS/RIS/EHR integrations and regulatory-ready validation documentation. We scope a fixed range after a discovery call once we understand your modalities, data volume, and target regulatory pathway.
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