AI development
for healthcare.
Healthcare has the richest data and the tightest constraints of any sector we work in. A model that would ship in a week elsewhere needs de-identification, audit trails, clinician sign-off, and a defensible answer to the question of what happens when it is wrong. We build for that reality rather than retrofitting it.
Discuss your projectHigh-value use cases.
Clinical documentation
Ambient capture and structured summarisation of encounters. The highest-ROI application in healthcare right now, because it attacks clinician burnout directly and errors are caught by the clinician who signs the note — a human review step that already exists in the workflow.
Medical imaging triage
Computer vision for prioritising studies rather than replacing reads. Framing it as triage rather than diagnosis changes the regulatory position substantially and is where most deployed value actually sits.
Coding and revenue integrity
Extracting billable codes from clinical text and flagging under-documentation. Well-bounded, financially measurable, and verifiable against adjudicated claims — which means you can prove the value.
Deterioration prediction
Early-warning scores from vitals and labs. Powerful and unusually sensitive to the base rate problem — a sepsis alert firing ten times per true case trains staff to ignore it.
Operational forecasting
Admissions volume, theatre scheduling, and staffing. No patient-safety exposure, immediate financial return, and usually the right place to start building institutional confidence.
Patient-facing triage
Symptom intake and routing. Requires hard scope limits, conservative escalation, and explicit refusal behaviour — the failure mode here is a confidently wrong reassurance.
Sector-specific constraints.
Generic AI advice fails here for specific, predictable reasons. These are the constraints that shape every design decision we make in healthcare.
- PHI governs architecture — HIPAA and equivalent regimes shape where data lives, who processes it, and what may reach a third-party API
- Explainability is not optional — clinicians will not act on unjustified output — see explainable AI
- Asymmetric error costs — a missed diagnosis and a false alarm are not equally bad; thresholds must reflect that
- Alert fatigue kills deployments — calibrate to what clinical staff can actually review, not to a statistical optimum
- Regulatory classification — software influencing diagnosis or treatment may be a regulated medical device
- Distribution shift between sites — a model trained at one hospital frequently underperforms at another
AI in healthcare.
Can we use a hosted LLM API with protected health information?
Often yes, but only under the right contractual and architectural conditions. You need a business associate agreement with the provider, assurance that your data is not retained or used for training, and a defensible data flow. Many teams reduce exposure further by de-identifying before transmission or by running an open-weights model in their own environment. The decision should be documented before the first prototype touches real records, not after.
How do we validate a clinical model safely?
Retrospective accuracy on historical data is the starting point, not the finish. Add validation on a temporally separate period, evaluation broken out by demographic and site subgroups rather than in aggregate, and a silent deployment phase where the model runs alongside clinicians without influencing decisions so you can compare its output to real outcomes. Only then consider a limited live rollout with monitoring and a clear rollback path.
Does a predictive model make us a medical device manufacturer?
It depends on the claimed function. Software that informs a clinical diagnosis or treatment decision may fall under medical device regulation in the US, EU, and UK, while operational and administrative tools generally do not. The distinction turns on intended use as you describe it, which means marketing language has regulatory consequences. Take a regulatory view early — it materially shapes what you build.
Other industries.
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ReadLet’s scope it
properly.
Tell us the problem you are trying to solve in healthcare and we will tell you honestly whether machine learning is the right tool.
Start the conversationor write to us at [email protected]