The opportunity in 2026 is real — ambient documentation, imaging triage, and admin automation are already saving clinicians hours a day. The constraint is just as real: clinical validation, HIPAA and GDPR, and an EU AI Act that treats most clinical AI as high-risk. We build healthcare AI that earns a place in the workflow because it holds up to scrutiny.
Healthcare has no shortage of promising AI pilots — and a long history of them stalling before they reach a ward. The constraints that decide success are specific to the sector: a system that works in a demo still has to be validated, governed, integrated, and trusted by clinicians who carry the accountability.
A high benchmark score is not clinical evidence. Diagnostic, triage, and decision-support tools need validation against real patient populations, prospective evaluation, and post-deployment monitoring — the gap most pilots never cross.
Under the EU AI Act, AI used for diagnosis, triage, and clinical decision support is high-risk — layered on top of MDR and IVDR, with HIPAA and GDPR underneath. Compliance is a design input, not a closing checklist.
Value lives inside the EHR, not beside it. Real deployments have to read and write through HL7 v2, FHIR, and CDA across Epic, Oracle Health, and legacy systems — a hybrid integration most pilots quietly skip.
A hallucinated drug interaction or omitted red-flag symptom is a patient-safety event. Generative tools need grounding, citation to source, and human oversight built in — not bolted on after the first incident.
2–3 hours a day
Independent reporting on ambient clinical documentation points to documentation-time savings on this order for clinicians, with sharp drops in after-hours charting — a direct response to burnout and one of the clearest near-term wins in healthcare AI.
The strongest 2026 cases sit where AI removes documentation and administrative load, or surfaces evidence a clinician then confirms. Imaging triage runs in over a thousand hospitals; ambient scribes are in routine use; prior-authorisation workloads are being cut by more than half. The pattern that works keeps a clinician accountable and the AI in support — and we build for exactly that division of labour.
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We work where the clinical and operational case is strongest and the path to production is clearest. Each of these draws on a Miniml capability — built, validated, and governed for a healthcare setting.
Ambient scribing and structured extraction from notes, letters, and discharge summaries — with the clinician confirming before anything is committed. Reported documentation-time savings here are among the clearest wins in the sector. Built on document intelligence →
Grounded question-answering over guidelines, formularies, and local protocols — every answer cited to the controlling document, scoped to the right jurisdiction and care setting. Built on knowledge & retrieval →
Triage and detection that prioritises urgent findings for a radiologist to confirm — the most mature category of cleared clinical AI, already running in over a thousand hospitals. Built on computer vision →
Agents that draft referrals, assemble prior-authorisation packs, and chase missing information — cutting administrative phone and paperwork load, with a human approving anything that leaves the building. Built on AI agents →
Models that flag readmission risk and forecast demand on beds, theatres, and clinics — so capacity and follow-up are planned, not reacted to, with the drivers behind every score made legible. Built on predictive analytics →
The control layer the rest depends on — validation evidence, human-oversight design, monitoring, and an audit trail that stands up to the EU AI Act’s high-risk regime. Built on AI governance →
Healthcare AI lives or dies on the things that come after the model works. We treat each of these as a design input from day one — not a hurdle to clear at the end.
Talk through your healthcare use case →Often, yes — and you should assume so until proven otherwise. Software that informs diagnosis, triage, or treatment usually meets the definition of a medical device under MDR or IVDR, and the EU AI Act adds a high-risk layer on top. Administrative and documentation tools frequently sit outside the device regime. We help you classify the use case early, because it changes the build.
Not unsupervised, and not for unconstrained clinical advice. They are genuinely useful for documentation, summarisation, and grounded question-answering over your own approved sources — where every answer is cited and a clinician confirms it. The failure mode that matters is the confident, plausible, wrong answer, and we engineer grounding and oversight specifically against it.
For high-risk clinical AI: a risk-management system, governed training data, technical documentation, logging, human oversight, and post-market monitoring — alongside your existing MDR or IVDR obligations rather than instead of them. Core requirements apply from August 2026, with an extended transition for AI already certified as a medical device. We design to these from the first sprint.
That is usually where the real work is. We build to read and write through FHIR APIs and HL7 v2 messaging across Epic, Oracle Health, and the legacy systems most estates still run — treating interoperability as core scope, not a final-week surprise. If value has to live inside the chart, integration is the project.
With a use case that has a clear owner, a measurable outcome, and a tolerable error profile — documentation, admin automation, or imaging triage before autonomous clinical decisions. Pick the one where a win is unambiguous and the safety case is manageable, prove it, then extend. We help you choose and scope it honestly.
Yes. The code, the models, the integrations, the validation evidence, and the governance artefacts — all yours. We design every engagement so your team can operate, audit, and extend the system independently. Enablement is built into the project, not sold back to you later.
We assess it against the criteria that decide whether healthcare AI reaches a ward: the clinical and operational case, the regulatory classification, the data and integration reality, the validation and oversight plan, and the safety case. You leave knowing whether it is worth building, what it takes, and where the risk sits.
What you get: a readiness assessment of your use case; an EU AI Act and medical-device classification view; a target architecture that fits your EHR, data residency, and oversight model; a validation and governance plan; and a staged delivery plan with effort estimates. One workshop with your clinical, data, and information-governance leads. Led by a senior consultant — fixed scope, fixed fee.
Book a consultation →A 30-minute conversation with a senior consultant. Bring a healthcare AI idea, or a pilot that has stalled on safety, regulation, or integration. We’ll tell you whether it’s worth building, what the path to production looks like, and where the real risk sits.
Book a consultation →