This system applies the ACMG/AMP 2015 + ClinGen SVI + Tavtigian point framework deterministically, gathers nine evidence layers in parallel, and layers advisory mechanistic interpretation on top. The clinician enters the pre-diagnosis and patient findings in Turkish, receives the report in Turkish, and can question the result through a Turkish-language dialogue. It does not process raw sequencing data (FASTQ, BAM), call variants, or act as a filtering panel — it starts where the mature platforms that do that work leave off: with a variant of uncertain significance already in hand.
For every case, nine evidence layers are gathered in parallel → six interpretation modules evaluate that evidence in biological context → the deterministic ACMG engine produces the scores and the class → the findings are synthesized into a clinician-facing assessment → a source-cited report is generated in the Turkish medical-genetics standard (HTML / Word / PDF) → and you can question the result, in Turkish, through a citation-verified RAG clinical assistant that draws on eight sources.
Variant + Turkish pre-diagnosis + Turkish patient findings. Personal data (KVKK) is stripped on entry.
From population to splice effect, collected in parallel from authoritative sources.
Deterministic Tavtigian points & tier resolution. AI never touches the class.
Six interpretation modules produce advisory reasoning; phenotype-match shows what it resolved Turkish findings to.
Lab-letterhead HTML / Word / PDF.
Eight sources are retrieved for citations; an unverified citation never reaches the answer.
Each card below reflects a real, live capability in the system. Two differentiators stand out: Turkish-first design and integrated Turkish population data.
The class is set by a rule-based engine following the field's accepted Bayesian framework (Tavtigian) — not by AI.
Nine independent layers run in parallel; each answers a specific clinical question.
Advisory modules that place the collected evidence in biological context; they never change the class.
Real cases usually carry more than one variant.
The clinician enters the pre-diagnosis in Turkish; the system resolves it to an international ontology through a controlled vocabulary of 10,572 Turkish disease names.
The clinician asks free-text questions; the answer is generated only from sources retrieved for that question — never from the model's own memory.
A local frequency database compiled at the aggregate level from 3,362 individuals, used alongside international data.
A report in the Turkish medical-genetics standard — lab-letterhead and customizable.
The items below are not live yet; they are part of our research and productization roadmap. The system's current operation is complete without them.
Canonical splice-site variants are already evaluated. A separate predictive model is being integrated for deep-intronic and exonic-regulatory variants; until then the system explicitly flags these as "not evaluated for splice."
A filter is in development to surface clinically meaningful secondary findings — by pathogenicity probability, not by pre-diagnosis. No variant is hidden today either.
For variants in genes not yet linked to any disease, a component in development will reason from the protein level: type of change, regional importance, and stability impact.
A component in development will establish relationships between variants in different genes from curated pathway/complex databases — never assumed by AI. It does not compromise the current gene-isolation safety principle.
A component in development will let each lab accumulate its own anonymized evaluation history, improving within-lab consistency.
50–100 real clinical cases, at least two independent reviewers, and a six-dimension rubric will compare the system-supported workflow against the current one. This is the project's most critical evidence gate.
The system currently runs on a single user. Making it multi-user — with role and permission separation, case handover, and shared review — is needed so that several specialists in the same lab can work at the same time.
Clinical labs manage variant data through their own information systems. Connecting the system to exchange data with those environments removes manual transfer and lets it fit inside the existing workflow.
The evaluation output is planned to be produced in line with Turkey's medical-genetics reporting standard and in each lab's own letterhead format. The goal is to eliminate the time a specialist spends on report formatting.
The system currently runs in production with its existing feature set.
When multiple labs share the same infrastructure, each institution's data must be isolated from the others. Access control, data segregation, and per-institution audit trails are mandatory components of this architecture.
Whether clinical decision-support software falls under medical-device regulation depends on how far the system steers the clinical decision. Making this determination — and, if required, defining the conformity path — is a precondition for productization.
The system draws on several third-party data sources and tools that are free for academic and research use (OMIM, FoldX, etc.). Offering the product commercially requires securing commercial-use licenses for these resources — one of the legal preconditions for productization.
The clinical-utility study to be run on real cases requires ethics-committee approval. The application will be submitted to the relevant board once the study design is finalized.
How the product will be offered to labs — institutional subscription, per-case usage, or a combination — has not yet been decided. The model is planned to be structured around labs' existing cost structures and case volumes.
The system has so far worked with a single clinical partner. Pilot use with labs of different sizes and workflows will both test the product's generalizability and build the case base for the clinical-utility study.
The ACMG class is produced by a locked, rule-based engine. The mechanistic interpretation (MIL) layer reasons about mechanism alongside that class — it supplies the "why", never the verdict.
ACMG/AMP criteria are scored and resolved into a class by a rule-based, deterministic engine following the field's accepted Bayesian framework (Tavtigian). The same input always yields the same class.
AI never touches this engine. Classification authority is entirely deterministic — this is the system's most fundamental architectural decision.
class = f(rules, evidence)The interpretation modules summarize literature and mechanism for the clinician; phenotype-match resolves Turkish findings to an ontology and shows what it resolved to.
Its output is strictly advisory: even the calibrated pathogenicity probability is presented as a qualitative range and cannot set, shift, reclassify or override the class. This is not a prompt instruction — it is how the system is built to run.
MIL interprets · rules classifyThe distinction between proven and unproven is one of the project's core working principles.
The clinical application is currently in a limited pilot phase. To see the pipeline in action or request a demo, get in touch — we'll get back to you shortly.
or directly: [email protected]
Note. It's a clinical decision-support system, not a diagnostic tool.