Case Studies

Challenge

The radiology team at a mid-size hospital was juggling five separate applications for literature search, DICOM viewing, report dictation, case discussion, and follow-up tracking. Residents lost 30–45 minutes per day switching contexts, and critical findings occasionally fell through the cracks during handoffs.

Approach

Deploy HALO Core as the central intelligence workspace, ingesting existing PACS data, PDF guidelines, and previous second-opinion reports into a unified knowledge base. Configure three coordinated agent profiles: a Research Agent for PubMed queries, a Report Agent for structured dictation, and a Follow-up Agent for tracking recommendations.

Solution

HALO Core's LanceDB vector store indexed 12,000 prior reports and 400 PDF guidelines. The Research Agent surfaced relevant literature in under 3 seconds; the Report Agent generated BI-RADS-compliant drafts; the Follow-up Agent created structured reminder tasks in the hospital's task manager via MCP.

Results

• ●Context-switching time reduced by 78% (from 38 min to 8 min per shift)
• ●Second-opinion report turnaround improved from 4 days to 1.2 days
• ●Zero missed follow-up recommendations in the first 6 months

Challenge

A community hospital faced a 40% increase in MRI volume after a neighbouring clinic closed its imaging department. With two radiologist vacancies unfilled, the backlog grew to 180 studies, and stat cases were delayed by up to 6 hours.

Approach

Integrate Corpus Analyzer with the existing Orthanc PACS via DICOMweb. Configure the Modality Detection and Radiology Context skills to auto-triage incoming studies by body region and urgency. Enable the AI Detection Pipeline skill to flag suspected acute findings for priority review.

Solution

Incoming DICOM studies were automatically routed: stat neuro studies flagged within 90 seconds, MSK queued for batch review, and non-urgent outpatient scans scheduled for next-day reporting. The radiologist could click any flagged study to see an AI-generated preliminary finding list with confidence scores.

Results

• ●MRI backlog cleared from 180 to 12 studies within 3 weeks
• ●Stat case delay reduced from 6 hours to 22 minutes median
• ●Radiologists reported 25% less cognitive load during peak shifts

Challenge

A health-tech startup had a working Streamlit prototype for diabetic retinopathy screening but no production infrastructure. Building auth, Stripe billing, German/English i18n, and GDPR audit logging from scratch would delay their CE-marking timeline by 4 months.

Approach

Rebuild the prototype on top of Corpus Core SaaS, leveraging the built-in Auth0 OAuth 2.0 module, Stripe subscription flow with free-trial logic, and the extensible i18n framework. Use the audit-logging middleware for GDPR Article 30 documentation.

Solution

The team replaced 80% of their custom infrastructure code with Corpus Core modules. Auth0 handled Google and Microsoft SSO; Stripe managed monthly/annual plans with automatic VAT invoicing; i18n covered German medical terminology out of the box. Docker deployment to Hetzner Cloud took one afternoon.

Results

• ●Time-to-market reduced from 5 months to 6 weeks
• ●Passed external GDPR audit on first attempt
• ●Customer onboarding time dropped from 3 days to 15 minutes

Challenge

A multidisciplinary pain clinic used paper body outlines where patients drew pain locations with coloured pens. During multidisciplinary conferences, physiotherapists misread 1 in 5 charts, and the EMR team spent hours digitising sketches for billing codes.

Approach

Deploy the PainTracker WordPress plugin on the clinic's existing patient portal. Configure male and female 3D body models with adjustable marker intensity. Integrate the JSON output into the physiotherapy management system via a custom webhook.

Solution

Patients now mark pain on a Babylon.js 3D model via tablet or smartphone. Each marker carries coordinates, intensity (1–10), and a free-text description. The JSON payload auto-populates the physiotherapist's treatment plan template and feeds directly into DRG coding.

Results

• ●Chart misreading errors eliminated (from 20% to 0%)
• ●Conference preparation time cut from 45 min to 5 min per patient
• ●Patient satisfaction with intake process rose from 62% to 91%

Challenge

A private radiology group received second-opinion requests via unstructured emails with DICOM CDs sent by post. Each case required 20–30 minutes of manual file organisation, patient data entry, and insurance verification before a radiologist could begin reading.

Approach

Deploy the Second Opinion platform with patient self-intake, document upload, and structured case storage. Integrate PainTracker for patients with chronic pain histories. Connect the notification system to the practice management software.

Solution

Patients now complete a guided intake form, upload DICOM directly via the browser (using DICOMweb), and receive automated status updates. The admin dashboard queues cases by urgency and completeness. PainTracker data, when present, appears as a 3D overlay alongside the DICOM study.

Results

• ●Case prep time reduced from 3 days (mail + manual entry) to 30 minutes
• ●Administrative staff workload decreased by 60%
• ●Patient complaint rate dropped by 45% due to transparent status tracking

Challenge

The editorial team at a medical publisher spent 3 hours every morning manually scanning English AI research blogs, translating key stories, and rewriting them for a German physician audience. Burnout was high, and deep-dive features were perpetually postponed.

Approach

Launch Gods in White as an automated news pipeline. Configure twice-daily web research with AI-based relevance scoring, German-language article generation with medical terminology accuracy checks, and auto-generated title images. Deploy via Netlify with GitHub Actions.

Solution

The pipeline runs at 06:00 and 14:00 CET. It scans 40+ sources, filters by healthcare AI relevance, generates 3–5 articles per run, and queues them for human approval. Approved articles auto-publish with Open Graph images and structured data.

Results

• ●Editorial production time reduced from 3 hours to 20 minutes per day
• ●Deep-dive feature output increased from 1 to 4 articles per month
• ●Physician newsletter open rate rose from 18% to 34%

Challenge

A university hospital's procurement committee spent 4–6 months evaluating each medical AI vendor. Setup of trial environments, API configuration, and security reviews consumed IT resources and delayed clinical adoption.

Approach

Use the Lab as a zero-setup evaluation environment. Invite vendors to deploy their agents as skills; let clinicians toggle radiology skills (PACS query, DICOM analysis, structured reporting) and observe real-time case reasoning with full traceability.

Solution

Vendors upload agent configurations as Docker containers. Clinicians run standardised test cases (e.g., lung nodule CT follow-up) through the Lab interface. Every reasoning step, API call, and confidence score is logged. The procurement committee reviews traces instead of slide decks.

Results

• ●Vendor-evaluation cycle shortened from 5 months to 2.5 months
• ●IT setup time per vendor reduced from 3 weeks to 2 days
• ●Clinical stakeholder engagement in procurement decisions rose by 70%

Challenge

A teleradiology firm employs 35 radiologists across 8 countries. Each used a different AI assistant (Claude, Cursor, Windsurf, OpenCode) with ad-hoc prompts, producing inconsistent report structure and occasional missed follow-up recommendations.

Approach

Install the complete Corpus Analytica clinical skills library (26 skills) on all four platforms. Configure radiology-context as a mandatory prerequisite for every session. Standardise structured-reporting templates for BI-RADS, LI-RADS, and PI-RADS across all agents.

Solution

Each radiologist's IDE or chat interface now loads the same context-aware skill set: Modality Detection auto-classifies studies, PACS Workflow retrieves priors, PubMed Search surfaces evidence, and Structured Reporting enforces compliance templates. Skills update centrally via `npx skillsadd`.

Results

• ●Report structure consistency improved from 64% to 97% across all radiologists
• ●Follow-up recommendation omissions dropped by 82%
• ●Onboarding time for new radiologists reduced from 2 weeks to 3 days

Challenge

A diagnostic imaging centre processed 300+ DICOM studies nightly for research archive transfer. Two FTEs manually downloaded studies, stripped PHI, ran quality checks, and generated transfer manifests — a 4-hour shift prone to human error.

Approach

Orchestrate the Corpus Analytica CLI suite into a single nightly pipeline: `fetch_study` downloads complete studies from the PACS, `dicom_anonymizer` strips patient identifiers while preserving clinical tags, `image_qc` assesses SNR and resolution, and `tat_analyzer` logs turnaround metrics.

Solution

A GitHub Actions workflow triggers at 02:00 CET. Studies are fetched via DICOMweb, anonymised with configurable tag maps, and QC metrics are written to a SQLite database. Failed QC studies trigger Slack alerts. Transfer manifests auto-generate as JSON for the research partner.

Results

• ●Manual QA time eliminated (from 4 hours to 12 minutes automated)
• ●PHI breach risk reduced to zero with automated anonymisation verification
• ●QC failure detection improved from 85% to 99.2% recall

Challenge

A regional hospital network operated three hospitals with separate Orthanc PACS instances, Epic Radiant EHR silos, and a standalone Aidoc AI triage server. Critical AI alerts never reached the EHR, and radiologists toggled between three interfaces to review flagged studies.

Approach

Deploy the Corpus Analytica integration layer with skills for PACS Workflow (Orthanc), EHR Context (Epic Radiant), and AI Triage (Aidoc). Use HL7 FHIR R4 for EHR communication and DICOMweb for PACS/AI routing.

Solution

Aidoc flags a suspected PE on a chest CTA → the integration layer auto-creates an Epic Radiant critical finding note → the radiologist receives a single alert with deep links to the study in OHIF Viewer and the patient chart in Epic. All events are logged via FHIR AuditEvent resources.

Results

• ●Critical finding notification time reduced from 12 minutes to 90 seconds
• ●Radiologist interface switching eliminated (3 → 1 unified view)
• ●Inter-hospital transfer of imaging data simplified from 2 hours to 5 minutes