The Library
Every AI use case in healthcare — documented, classified, comparable.
The AIH Lab Repository organises real-world healthcare AI into 18 clinical families, so any clinician, hospital, regulator, or researcher can find, compare, and learn from what works.
- Published use cases
- 27
- Validated + scaled
- 6
- Countries represented
- 6
Healthcare AI arrives in fragments. AIH Lab gives it a map.
Three forces make healthcare AI hard to use today — and AIH Lab addresses each one structurally.
Fragmentation
Every hospital documents its AI differently. Comparing across organisations is nearly impossible.
Regulation
The EU AI Act made healthcare AI a high-risk category. Most hospitals lack the structure to show classification and oversight.
Trust
Clinicians, patients, and the public need clear answers about what AI is doing, with what evidence, under what oversight.
18 use case families
Healthcare AI does not arrive in neat packages. A family is a clinical concept; every local deployment is an instance of it. The taxonomy makes hundreds of disconnected records comparable.
- 12 validated
Diagnostic Imaging & Pathology
4 instances
- 2
Clinical Decision Support
1 instance
- 3
Predictive Risk Stratification
1 instance
- 41 validated
Surgical Planning & Navigation
1 instance
- 5
Medication Safety & Optimisation
1 instance
- 6
Genomics & Precision Medicine
1 instance
- 71 validated
Mental Health & Neurology
2 instances
- 81 validated
Chronic Disease Management
1 instance
- 9
Patient Monitoring (ICU/Ward)
1 instance
- 10
Workflow & Operational Efficiency
2 instances
- 11
Administrative Automation
2 instances
- 12
Care Coordination & Pathways
1 instance
- 13
Patient Engagement & Communication
1 instance
- 14
Population Health & Epidemiology
1 instance
- 151 validated
Emergency & Trauma
3 instances
- 16
Rehabilitation & Allied Health
1 instance
- 17
Research & Clinical Trials
2 instances
- 18
Supply Chain & Asset Management
1 instance
From submission to validated deployment
Every use case travels a structured pipeline. Each stage builds trust, evidence, and replicability.
- 1
Submit
Any stakeholder registers a use case instance.
- 2
Curate
The AIH Lab team reviews completeness and confirms the family.
- 3
Mature
In-depth clinical, technical, ethical and operational review.
- 4
Validate
Formal real-world validation and the Assurance Pack.
- 5
Scale
Packaged for replication across other organisations.
One platform, every stakeholder
What you see first depends on who you are. The data is the same; the AIH Lab surfaces the part of it you need.
Clinicians
See what's validated, how oversight stays human, and what safety signals are open.
ExploreHospital leaders
Compare deployments, find replicable Assurance Packs, plan with EU AI Act in mind.
ExploreRegulators
Audit trail, classification mix, equity coverage — exported in JSON or CSV.
ExploreResearchers
The pipeline of healthcare AI and the evidence record on file, family by family.
ExplorePatients & the public
Plain-language summaries of every validated AI deployment, with a way to raise concerns.
ExploreAI assists the work — humans decide.
AIH Lab uses AI in three places: completeness checks at submission, family suggestions during curation, and synthesis at the Hub. Every machine output is labelled. Every decision is human.
How the AI worksBuilt for trust
Three structural commitments shape every feature.
- • No patient data — deployment metadata only.
- • Full audit trail — every transition logged.
- • Aligned with EU AI Act — Assurance Packs map to obligations.
Evidence status is always explicit
No record appears without a trust badge derived from its pipeline stage. You always know what you are looking at.