AI breast cancer detection at scale

What we'd propose

• Enterprise AI

  Unified Diagnostics Data Platform for Longitudinal Patient Monitoring

  We build an ontology-based data platform that integrates diagnostic pod sensor data, AI analysis results, patient health records, and telemedicine consultations into a unified architecture — enabling longitudinal patient monitoring across deployed pods and hospital systems, with the data governance model required for GDPR and HIPAA compliance.

  • Pod telemetry and AI results ingestion

    Every pod connected, every result accessible

    Deploy a validated data ingestion layer that receives diagnostic pod sensor data and AI analysis outputs from every deployed pod globally, normalises them to the unified data model, and makes them available for longitudinal patient monitoring and algorithm retraining — regardless of the pod's location or the hospital IT system it is connected to.

  • Hospital EMR and PACS integration

    Results delivered to the radiologist's existing workflow

    Build integration adapters for the HL7 FHIR and DICOM standards used by hospital radiology information systems, so that AILIS diagnostic results are delivered directly into the radiologist's existing PACS or EMR workflow — eliminating the friction of a separate platform login and increasing clinical adoption rates.

  • Longitudinal monitoring and baseline tracking

    Each patient seen over time, not in isolation

    Implement a longitudinal monitoring engine that tracks each patient's diagnostic history across all pods and hospital visits — enabling the AI system to compare new results against a personal baseline rather than only population-level reference data, which is particularly valuable for women with dense breast tissue where within-patient change is more informative than single-measurement comparison.

  • Radiologists receive AILIS diagnostic results within their existing workflow, removing the friction that causes them to defer to their own imaging rather than incorporating the AI diagnostic output.
  • Longitudinal monitoring enables the AI to detect change from a personal baseline — a more sensitive indicator of early malignancy than population-level reference data, particularly for women with dense breast tissue.
  • The data platform provides the evidence base for algorithm accuracy across demographic cohorts, directly addressing the regulatory concern that reviewers will raise during CE-MDR and FDA submission review.

  We've done this before

  • Transforming Raw Bioprocess Data into Actionable Insights
  • Real-Time Process Intelligence & KPI Visualization

  See how we do Enterprise AI
• Digital CDMO

  ISO 13485-Compliant Manufacturing Automation for the AILIS Diagnostic Pod

  We implement manufacturing automation and quality management systems for the AILIS pod production line — digitising production workflows, automating quality control, and ensuring full traceability from component to deployed pod to meet ISO 13485 and EU MDR requirements at scale.

  • Electronic batch records for pod production

    Every pod fully traceable from components to deployment

    Deploy an MES with electronic batch records that captures the complete production history of each AILIS pod — component serial numbers, assembly steps, calibration results, and QA sign-offs — creating an ISO 13485-compliant traceability record that can be retrieved for any pod at any time.

  • AI-assisted visual inspection for DPI sensor arrays

    Defects detected automatically during assembly

    Implement computer vision inspection at key assembly steps for the DPI sensor array — detecting defects, misalignments, and calibration drift automatically during production rather than during final test — reducing rework cost and ensuring that each pod meets the sensor performance specification before it leaves the factory.

  • Supplier quality management integration

    Component quality tracked from qualified suppliers

    Extend the MES to cover supplier quality management for the key components of the AILIS pod — DPI sensors, AI processing units, ergonomic seating — with incoming inspection records, supplier non-conformance tracking, and automated alerts when a component批次 is associated with field failures.

  • ISO 13485 and EU MDR audit readiness is achieved because every pod's production history is captured in electronic batch records — the traceability evidence that regulatory inspectors require is a retrieval rather than an assembly.
  • Manufacturing throughput increases because AI-assisted visual inspection catches defects at the point of assembly rather than at final test, reducing the rework cycle that limits production line speed.
  • Supplier quality issues are identified and contained before field failures occur, protecting patient safety and the brand reputation that the consumer-facing deployment model makes visible.

  We've done this before

  • Robotic Filling Line with Advanced Servo Control & MES Integration
  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES

  See how we do Digital CDMO
• Digital Lab

  Remote Diagnostic and Support Infrastructure for Distributed Pod Fleet

  We establish secure remote connectivity and diagnostic capabilities for the globally distributed AILIS diagnostic pod fleet — enabling rapid incident diagnosis, proactive maintenance, and software updates from central engineering teams without requiring on-site visits for the majority of fault conditions.

  • Secure pod connectivity platform

    Every pod connected to the remote diagnostics centre

    Deploy a validated secure connectivity layer for the AILIS pod fleet — covering remote access, telemetry streaming, and software update delivery — so that the central engineering team can access pod status, diagnostic data, and system logs from any deployed pod without requiring physical presence.

  • Predictive maintenance for pod fleet health

    Pod failures predicted before they affect patient appointments

    Build a predictive maintenance model that analyses pod telemetry data — sensor calibration drift, thermal trends, AI processing unit load patterns — to identify pods that are approaching a fault condition before the fault occurs, enabling proactive service scheduling that avoids patient appointment disruption.

  • AR-guided remote field support

    On-site technician guided by remote specialist

    Integrate an assisted-reality layer (RealWear or equivalent) for the rare cases that require on-site intervention — enabling the on-site technician to share a live feed with the central specialist and receive step-by-step visual guidance through the repair procedure, reducing mean time to repair for complex faults.

  • The service model becomes economically sustainable at scale because the majority of pod faults are diagnosed and resolved remotely — the central engineering team's capacity is multiplied across the global fleet without proportional growth in service headcount.
  • Patient appointment disruption is reduced because predictive maintenance identifies pods approaching fault conditions before they fail, enabling proactive service scheduling.
  • Mean time to repair for complex faults is reduced because the AR-guided support layer connects on-site technicians with central specialists who can see exactly what the technician sees.

  We've done this before

  • Support Framework & Assisted Reality for Global Biotech Manufacturer
  • Lifecycle Management for Adaptive Biotech Assets

  See how we do Digital Lab
• Digital Lab

  Regulatory Compliance Digitisation for CE-MDR and FDA Submissions

  We implement digital compliance management systems for the AILISCARE regulatory submission process — automating clinical evidence documentation, building audit-ready records, and providing the structured data submission package that CE-MDR and FDA reviewers require.

  • Clinical evidence management platform

    All clinical data organised for submission

    Deploy a clinical evidence management system that organises all clinical study data, imaging results, and algorithm validation results into the structured format required for CE-MDR and FDA technical documentation — replacing the manual document assembly that currently creates timeline risk before submission deadlines.

  • Automated audit trail for clinical data

    ALCOA+ compliance verified continuously

    Configure automated ALCOA+ compliance verification across all clinical data — including data from pods deployed during the medical experiment phase — with continuous audit trail recording, attribution verification, and automated flags when data completeness or attribution falls below the standard that regulators require.

  • Regulatory submission data package generator

    Submission packages assembled from validated records

    Build a submission data package generator that extracts clinical evidence, technical documentation, and algorithm validation results from the structured evidence management system and formats them into the specific structure required by EU MDR and FDA 510(k) reviewers — reducing the retrospective assembly effort that typically creates last-minute timeline pressure.

  • The regulatory submission timeline is protected because the submission package is a structured retrieval from a validated database rather than an assembly from disparate sources — the last-month scramble to compile documentation does not happen.
  • The ALCOA+ compliance evidence is continuously maintained rather than retrospectively compiled, so there is no last-minute discovery that data is incomplete or attribution records are missing.
  • Post-market surveillance obligations under CE-MDR are continuously met because the compliance management system automatically tracks the ongoing evidence requirements that attach to the device after market approval.

  We've done this before

  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES
  • Lifecycle Management for Adaptive Biotech Assets

  See how we do Digital Lab
• Digital Lab

  AI Algorithm Validation Platform for Demographic Coverage

  We build a clinical data aggregation and algorithm validation platform that collects de-identified diagnostic data from all deployed pods, tracks demographic coverage gaps, and provides a systematic framework for demonstrating algorithm accuracy across the full range of intended populations — the evidence CE-MDR and FDA reviewers require to approve an AI diagnostic device.

  • Demographic coverage tracking dashboard

    Training data gaps visible and addressable

    Build a dashboard that tracks the demographic composition of the clinical data collected by all deployed pods — age distribution, breast density categories, geographic coverage — highlighting the cohorts where algorithm accuracy is statistically strongest and where additional training data is needed to achieve comparable performance.

  • Federated learning for algorithm improvement

    Global pod data improves the algorithm without sharing raw data

    Implement a federated learning architecture that allows the AI model to improve from pod data without requiring patient-level data to leave the hospital or pod location — satisfying GDPR data minimisation requirements while enabling the algorithm accuracy improvements that depend on larger, more diverse training datasets.

  • Algorithm version management and regulatory traceability

    Every diagnostic result tied to a specific validated model version

    Configure an algorithm version management system that ensures every diagnostic result produced by the AILIS pod is traceable to the specific validated model version that generated it — a regulatory requirement for AI-based medical devices that is technically complex to implement but essential for post-market surveillance.

  • CE-MDR and FDA reviewers receive systematic evidence of algorithm accuracy across demographic cohorts — the systematic demonstration of equity that regulators increasingly require for AI diagnostic devices.
  • The federated learning approach provides algorithm improvement without the data governance complexity that centralised training data collection creates under GDPR — the algorithm improves while patient data stays where it was collected.
  • Algorithm version traceability means that when a field diagnostic result is questioned, the exact model version that generated it is known — a prerequisite for post-market surveillance and for managing the algorithm update process under MDR.

  We've done this before

  • Bridging the Gap Between Scientists and Algorithms
  • Fully Automated Autosampling Control System

  See how we do Digital Lab