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, correction and now document generation 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 needed, MNSE Generative now writes it: a maximum-a-posteriori template render over learned section models, with no sampling, so the same record always produces the same document. 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.
Corrupted, garbled and mis-sectioned scans of real discharge packets — 99.97% held on an unseen 3,000-case holdout.
Extraction was only half the problem. MNSE now composes the clinical document itself — deterministically, with every sentence traceable to a source span.
A photographed or PDF'd discharge packet — skewed, garbled, sections out of order — becomes a clean, structured summary: diagnoses, procedures, investigations, dated medications with dose and frequency, condition at discharge and follow-up.
Prose is produced by a maximum-a-posteriori template mixture over learned section models. There is no temperature and no sampling, so the same document renders the same way every time — a property no language model can offer.
A Bayesian completeness verifier scores every rendered summary and either commits it or escalates. MNSE returning nothing is a designed outcome: it will not issue a document it cannot substantiate.
SNOMED CT, ICD-10 and LOINC were mined into diagnosis, procedure, lab and substance substrates, then used to synthesise ten thousand realistic adversarial records — the corpus the engine was hardened against.
Every value in a generated summary is carried from a span in the source document. The engine has no capacity to invent a drug, a dose or a date that was not written down.
Most of what an enterprise clinical assistant is asked to do is closed-world. MNSE answers that half outright, and hands the model only the questions that genuinely need one.
Twelve intents — open a view, launch a sub-agent, pull up a patient, generate a document, run a procedure guide — resolved deterministically from the doctor's words, with no model round trip.
MNSE · $0Counts, daily averages, busiest days, top diagnoses. The aggregation already holds the exact answer, so MNSE reports the numbers instead of paying a model to read them back — and cannot misquote them.
MNSE · $0Discharge summaries, prescriptions, clinical notes and referrals composed from the record under a coverage gate, benchmarked line by line against a 3,280-patient reference corpus.
MNSEOpen clinical reasoning, novel phrasing, genuine synthesis across free text — MNSE declines these by design and routes them to the language model. Knowing the boundary is the feature.
LLMThese 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.
SNOMED, ICD-10 & LOINC mined into diagnosis, procedure, lab and substance indexes.
Closed-world resolution of agent commands to twelve intents with typed slots.
Turns dictation into third-person clinical prose — speech and speaker turns removed.
Reports aggregation results exactly; declines anything requiring inference.
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.
MNSEThe discharge summary itself, written deterministically.
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.