Large language models are brilliant generalists that guess with probability. MNSE doesn't guess. It's a purpose-built neuro-symbolic engine for medicine — a small neural compiler fused with a deterministic symbolic resolver grounded in real drug, ICD-10, LOINC and SNOMED knowledge. The result: clinical-grade extraction and correction that is auditable, hallucination-free in scope, runs on a single CPU, and costs $0 per call.
For a narrow, high-stakes domain like medicine, a 175-billion-parameter generalist is the wrong tool. MNSE is the right one.
Trained on the whole internet to do everything, owned by no one
A small neural compiler fused with a symbolic medical resolver
MNSE is engineered to complement generative AI, not erase it. It owns the deterministic, repetitive, high-volume work (extraction, term-correction, drug safety) — and hands free-text prose generation to an LLM only where genuine language generation is required. Best of both worlds: the LLM writes; MNSE verifies.
Messy speech goes in. Structured, formulary-grounded clinical facts come out — through three deterministic layers.
A tiny CPU-only encoder turns noisy transcripts and STT garble into discrete concept candidates with confidence scores — classifying every span as drug, lab, diagnosis, vital, frequency, schedule or other. It hits 99.91% validation accuracy across 88,810 examples, yet featurizes and runs in ~42 ms.
Concept candidates are snapped to canonical medical truth by a battery of deterministic substrates — bitmap algebra, lexicons and reference-range engines resolving in ~125 microseconds. This layer physically cannot emit a drug outside the formulary, a lab outside LOINC, or a diagnosis outside ICD-10. It either answers with proof, or returns nothing.
Verified facts are assembled into 16 clinical fields — diagnosis, complaints, medications with typed dosing, investigations, follow-up and more — populated verbatim and traceably. Where free-text prose is genuinely needed, a generative model is fed only this clean, structured, PII-safe input. The doctor stays in control of the narrative.
Every figure below is measured against frozen, version-locked benchmarks in the production repository.
9,811 of 10,000 cases passed at the latest deterministic-loop checkpoint.
Phonetic + perturbation + fuzzy matching across 174,783 indexed Indian brands.
686 / 686 engineered adversarial drug attacks recovered.
Switching extraction to MNSE lifted overall field agreement 2.5× — diagnosis 37→76, drugs 2→64, examination 1→54.
These are high-frequency operations that ran on a metered LLM. MNSE took them over — at a marginal cost of zero.
Now fully MNSE-authoritative in production. The certified 98.1% engine corrects every transcript before the doctor ever sees it.
MNSE skips the LLM extraction call entirely and parses 16 structured fields itself — live on production today.
Repairs every drug brand in OCR'd discharge summaries before and after prose generation — at no marginal cost.
Drug correction alone accounted for roughly a third of the generative-AI spend. MNSE eliminated it.
Cost is tracked in the live usage dashboard, where MNSE is recorded as a $0 / zero-token provider distinct from metered LLM calls. The ~600× and ~33% figures reflect engineering measurements and deployment accounting; exact savings scale with your call volume.
Each substrate is a specialised, deterministic resolver — composable, testable, and grounded in curated medical knowledge.
174,783-brand formulary, phonetic + fuzzy matching, hallucination guardrail.
Pregnancy, renal, hepatic & drug–drug interaction bitmaps from RxNorm / openFDA.
NLEM-2022 essential medicines + India-specific contraindication rules.
ICD-10 resolution with a fuzzy span scanner over a curated clinical seed.
LOINC analyte resolution, reference ranges, and HIGH / LOW / CRITICAL flags.
BP, pulse, SpO₂, temperature, weight, RR, RBS/FBS with clinical flagging.
Natural-date parsing + OB/GYN procedure lexicon → appointment fields.
BID / TID / OD / PRN / HS dosing, duration, route and meal relation.
90k+ concept hierarchy — disorder, finding, procedure & observable buckets.
Char-n-gram TF-IDF tier for misspellings & STT garble — under 1 ms.
MNSE runs the deterministic spine of a real, full-scale clinical platform — every day.
Doctor speaks naturally; transcript streams in.
MNSE repairs medical terms & drug brands at $0.
MNSE16 structured fields parsed — no LLM call.
MNSEEvery drug snapped to the formulary & safety-checked.
MNSEClean, auditable record — ready for the doctor to sign.
MNSE is available as a standalone, embeddable clinical-intelligence engine — drop it into any HIS, EMR or scribe and replace metered LLM calls with deterministic, auditable, $0 automations.