Compare use cases
Drop two or three slugs into the URL — for example ?slugs=demo-mia-breast-screening,demo-idx-dr-retinopathy — or pick from the validated catalogue below.
Pick from validated:
AI imaging triage for the acute stroke pathwayAI second reader for breast cancer screeningAutonomous diabetic retinopathy screening in primary carePre-operative risk-stratification model for major surgerySelf-referral assistant for NHS Talking TherapiesType-2 diabetes long-term outcome predictor
Clear selection| Self-referral assistant for NHS Talking Therapies United Kingdom | AI second reader for breast cancer screening United Kingdom | Pre-operative risk-stratification model for major surgery Spain | |
|---|---|---|---|
| Trust badge | Validated | Validated | Validated |
| Evidence strength | Developing evidence | Emerging evidence | Robust evidence |
| Pipeline stage | Validated | Validated | Validated |
| Intended use | Supports patient self-referral by collecting clinical information conversationally and routing referrals, reducing administrative wait time. | Acts as an independent second reader of screening mammograms to support the double-reading workflow and reduce reader workload without lowering cancer detection. | Computes a 30-day mortality and morbidity risk score from pre-operative records to support anaesthetic optimisation and shared decision-making. |
| Specialty | Psychiatry | Radiology | Surgery |
| Autonomy | Human in the loop (human acts) | Informs a human (advisory) | Informs a human (advisory) |
| Deployment | Active deployment | Active deployment | Active deployment |
| EU AI Act tier | Limited risk | High-risk | High-risk |
| EU MDR class | Not a device | Class IIb | Not a device |
| CE marking | Not required | CE marked | Not required |
| NICE ESF tier | Tier B — inform / monitor | Tier C — treat / diagnose / calculate risk | Tier C — treat / diagnose / calculate risk |
| Headline metric | Accuracy: 0.93 | Sensitivity: 0.91 | AUC / AUROC: 0.85 |
| Subgroup assessed | Yes | Yes | Yes |