AIMedLab.proAI Research in Medicine
Our approach

Research-grade AI, built step by step

Every project starts with your research question, not with a technology. Here is how we work and what you can expect along the way.

Process

Five stages, full transparency

  1. 1

    Understand the research question

    We meet your clinicians or scientists, study the hypothesis, endpoints, data sources and constraints, and check whether AI is the right tool at all.

  2. 2

    Data audit & study design

    We assess data quality, volume and labelling, agree on a validation strategy and success metrics, and plan de-identification and access.

  3. 3

    Model development

    We prepare the data, build baselines and iterate on models, showing you interim results at every stage. No black boxes.

  4. 4

    Validation & interpretation

    We validate on held-out and external data, check calibration, bias and explainability, and perform the statistical analysis your reviewers will expect.

  5. 5

    Delivery & support

    You receive a working solution, documented code, a technical report and support for publication, deployment or regulatory submission.

Principles

What makes medical AI trustworthy

Patient data stays protected

De-identification, minimal data access, encrypted transfer, and on-premises deployment inside your infrastructure when required. Our processes follow GDPR and HIPAA principles.

Scientific rigour

Pre-agreed metrics, proper train/validation/test splits, external validation where possible, and reporting aligned with TRIPOD-AI, CONSORT-AI and similar guidelines.

Explainable by design

Clinicians need to understand why a model makes a prediction. We provide feature attributions, saliency maps and error analysis, not just a single accuracy number.

Regulatory-aware engineering

If a research model could later become a product, we document data lineage, versioning and testing to support a future quality-management and regulatory process.

Deliverables

What you receive at the end

Working AI model

Trained, versioned and tested, ready for your environment.

Reproducible pipeline

Clean, documented source code. You own all of it.

Technical & statistical report

Methods, metrics, validation results and limitations.

Publication support

Figures, tables and methods text for your manuscript or grant.

Deployment

On-premises, private cloud or integration into existing systems.

Knowledge transfer

Workshops so your team can maintain and extend the solution.

Technology

Modern tools, chosen for the task

We are not tied to one vendor or framework. We pick what gives the best result under your constraints: data volume, hardware, privacy requirements and time.

Deep learningComputer visionLarge language modelsRAG systemsGraph neural networksTime-series modelsSurvival analysisCausal inferenceExplainable AIFederated learningOn-premises deploymentMLOps & versioning

Have a research question?

Tell us about your study, data and goals. We will reply with an initial assessment and a proposed approach. The first consultation is free.