Brute-force AI is expensive
Pushing entire patient records through large models burns compute, memory, storage, and network on data that has nothing to do with the question at hand — and the inference bill scales with data volume.
The intelligence is in
knowing what not to process.
Patent pending · IN 202641072734
ConditionUncomplicated UTI · antibiotic course
TrajectoryOn track · AI suppressed
Model runs avoided0
Gate modesuppressed
Rules
Small model
Condition model
Large model
Illustrative engine behaviour — most milestones confirm quietly; only a deviation wakes a model, and only the cheapest sufficient one.
0model executions while a patient is on track
0inference tiers — deterministic rules to large model
0currencies on every inference: tokens, watts, dollars
0of decisions carry an explainable basis
The problem
Healthcare AI processes far more
than it needs to.
Follow-up is static
Fixed questionnaires and generic care plans ignore what each patient actually needs right now — given their age, comorbidities, risk, and where they are on their recovery journey.
Unrelated signals contaminate
A diabetic reports chest pain; a wound patient develops neuro symptoms. Conventional pathways either ignore the signal — or re-run AI over everything, every time.
How it works
A control system,
not another model.
The inventive idea is simple: use a milestone-specific minimal clinical signal set as a gate on computation itself. Trajectories derive from clinical guidelines where they exist — and where they don't, an intuitive designer lets the physician define the trajectory their patients usually follow.
The minimal signal set
For every milestone of a patient's expected recovery trajectory, MedNeuron derives the handful of guideline-backed data points actually needed to confirm the patient is on track. Not the full record. Not another 40-question form. Just what matters, when it matters. And where no clear guideline exists, the physician designs the trajectory in an intuitive visual editor — encoding the course their patients actually follow — and the gate treats it exactly like a guideline-derived one.
The inference gate
While those signals confirm expected progress, the system suppresses AI model execution entirely and simply logs the confirmation. Only on deviation does it escalate — selecting the least costly sufficient tier, from deterministic rules to a small local model to a large model. Red-flag signals override everything for safety.
The receipts
A resource-footprint ledger itemizes everything avoided — model runs, lab tests, imaging, in-person visits, patient travel — and converts computation into energy, cost, and carbon terms, down to a tokens-per-watt-per-dollar measure with auditable provenance. Efficiency isn't a claim we make; it's a number the system prints.
Patent-pending breakthroughs
Three ideas that change what clinical AI costs —
and how patients decide.
Shared decision-making · natural-history reference
Show the patient what happens with — and without — treatment.
Alongside every treatment trajectory, MedNeuron generates a time-indexed model of the untreated course: progression stages, complications, and their likelihoods. Generated once, at zero ongoing inference cost — a static reference you can put on screen during consent. Clinical terms for the physician, plain language for the patient. Honest, not alarmist: where the natural course is benign, it says so.
- Side-by-side divergence view
- Physician & patient modes
- Zero ongoing inference
Treatment-emergent sub-streams · TESS
An expected side effect shouldn't trigger a whole new workup.
When a symptom is explained by the active regimen — nausea on an antibiotic — MedNeuron keeps it in-stream as a lightweight sub-stream with a resolution window tied to the regimen. It escalates to a full pathway only if it persists past tolerance or crosses a severity threshold. Every branch avoided is one model call, one questionnaire, and one visit that never happened.
- Kept in the current trajectory
- Auto-resolves on schedule
- Promotes only on breach
Efficiency as a control input · TPWD
Every inference priced in watts and dollars — then used to pick the cheapest sufficient model.
MedNeuron converts every token used and avoided into energy and cost with auditable provenance — measured, billed, or estimated. The gate doesn't just record the metric; it acts on it, selecting the least watt-dollar model that still clears the clinical bar. Safety always overrides: a red flag forces the large model regardless of cost.
- Energy + cost per episode
- Cheapest sufficient mode
- Red-flag override
The platform
Everything needed to gate clinical computation.
Minimal Clinical Signal Set
Per-milestone signal selection derived from the disease-symptom complex — the smallest progress-validating subset, with red-flag exclusions.
Tiered Inference Gate
Deviation magnitude maps to the least-costly sufficient mode, from deterministic rules up to a large model — never more compute than required.
Adaptive Time-Granularity
Collection cadence tightens on risk and loosens when milestones are met — across self-report, exam, lab, imaging, and device channels.
Clinical Event Branching
Unrelated signals spawn independent, cross-linked pathways with their own signal set and cadence — no contamination of the active trajectory.
Resource-Footprint Ledger
Quantifies avoided model invocations, labs, imaging, visits, travel, and compute — with configurable CO₂e conversion factors.
Explainable by design
Every suppression, escalation, and branch carries its basis: guideline source, milestone threshold, and why each signal was requested or withheld.
MedNeuron supports the treating clinician; it never diagnoses or treats on its own. Physicians review and approve every trajectory — or design their own in the trajectory editor when guidelines don't fit their patients.
Intellectual property
Filed. Specified. Defensible.
FORM 2 · THE PATENTS ACT, 1970
PATENT PENDING
“A Computer-Implemented System and Method for Dynamic Minimal Clinical Signal Selection and Time-Granular Patient Trajectory Validation”
What the filing protects
The anchor
The computation gate
The minimal clinical signal set decides whether — and how much — computation runs. Claimed as a system, a method, and a medium.
The proof
The resource-footprint ledger
Avoided model runs, orders, visits, and travel, measured against a reference pathway — the savings the system prints itself.
The extension
Treatment-emergent sub-streams
Expected side effects tracked inside the active trajectory, with every avoided pathway tied back to the ledger.
The control
Watt-dollar model selection
Provenance-tagged energy and cost profiles used by the gate to pick the cheapest clinically sufficient model.
The claims cover the mechanism, not a model. The anchor: using the clinically minimal signal set as a gate on whether and how much computation runs. Around it, the filing protects the ledger that proves the savings, the sub-stream handling of expected treatment effects, and energy/cost-aware model selection.
The specification argues a measurable technical effect — reduced processor, memory, storage, network, and model-execution load — recorded by the system's own resource-footprint ledger.
Request the investor briefBetter together
Out of the box on HealthPilot.ai Coming soon
MedNeuron will ship out of the box on HealthPilot.ai — the intelligent operating system for modern healthcare, from the same company and already running in hospitals. The integration is in active development: trajectories generated at prescription, minimal signals collected through the patient portal and WhatsApp channels, and the resource ledger reported alongside billing — behind every EMR, follow-up, and patient-engagement workflow.
Running a different stack? MedNeuron is built as a control layer behind any EHR, CDS, or remote-monitoring product via a clean REST API.
Why investors should care
The economics of AI monitoring
are upside-down.
Continuous AI over full patient records means cost scales with data volume — and healthcare produces more data per patient every year. MedNeuron inverts the model: cost scales with deviation, and most patients, most days, are on track.
Defensible
Patent-pending architecture anchored on the computation gate, with an auditable resource ledger to prove it. A control layer is not another model to commoditize.
Measurable
The resource ledger reports avoided compute, avoided tests and visits, and CO₂e per patient — unit economics you can audit, not estimate.
Timely
Every health system deploying AI is about to meet its inference bill. MedNeuron is built for that moment — with distribution planned inside HealthPilot.ai.
Early access
Be first through the gate.
Request early access, the investor brief, or a walkthrough with the founder. We'll come back to you personally.