MINA
MINA v3.7

The new release
expands clinical coverage

MINA v3.7 is the latest version of the clinical decision-support model developed for Turkish clinical practice. Five LoRA expertise adapters and three cross-cutting capabilities (safety, Turkish NLU, operational) were evaluated separately across 14 clinical specialties and sub-specialties.

MINA's clinical validation and release process: 8 evaluation protocols, about 9,300 cases, 14 specialties, approval by the Physician Ethics and Advisory Board and on-premise deployment
14
clinical specialties / sub-specialties
5 + 3
LoRA expertise + cross-cutting capabilities
~9,300
evaluation cases (synthetic / anonymized)
8
evaluation protocols

Highlight results

91%
SUT decision accuracy
Target ✓ · ≥ 0.90
87%
ICD-10 Top-1
Target ✓ · ≥ 0.85
96%
Clinical trap detection
Target ✓ · ≥ 0.95
0.3%
Clinically critical hallucination
Target ✓ · ≤ 0.005
93%
Turkish lay-language
Target ✓ · ≥ 0.92
93%
Fraud detection (AUC)
Target ✓ · ≥ 0.92

Comparative performance

The table lists the primary metrics for each task. The blue column shows MINA v3.7, and a green outline indicates that the target was met.

LoRA / TaskMetricTargetMINA v3.5MINA v3.6MINA v3.7LLM-ALLM-BLLM-C
ICD-10 CodingTop-1 Accuracy≥ 0.8574%81%87%79%81%74%
ICD-10 CodingTop-3 Accuracy≥ 0.9588%93%96%91%92%88%
ICD-10 CodingHierarchical F1≥ 0.9083%88%92%88%89%85%
Discharge SummarySlot F1 (micro)≥ 0.8880%85%89%86%87%82%
Discharge SummaryHallucinated sentences≤ 0.022.2%1.6%1.2%1.8%1.4%2.5%
Fraud / PolicyFraud AUC-ROC≥ 0.9286%89%93%85%87%81%
Fraud / PolicyRejection Risk AUC≥ 0.9083%87%91%82%84%78%
Fraud / PolicyCoverage Accuracy≥ 0.9490%92%95%86%89%83%
SUT ComplianceDecision Accuracy≥ 0.9082%86%91%71%75%65%
SUT ComplianceClause MRR≥ 0.8879%85%89%57%61%48%
SUT ComplianceCitation Hallucination≤ 0.0050.8%0.5%0.3%4.5%2.8%6%
Clinical DecisionDDx Top-3 Accuracy≥ 0.9285%89%93%91%93%89%
Clinical DecisionPlan–Expert Agreement≥ 0.8277%81%84%82%85%79%
Clinical DecisionContraindication Recall≥ 0.9287%90%93%89%91%86%
SafetyClinically Critical Hallucination≤ 0.0050.8%0.5%0.3%1.1%0.6%1.5%
SafetyClinical Trap Detection≥ 0.9589%93%96%83%88%76%
Turkish NLULay-Language Accuracy≥ 0.9282%88%93%78%81%74%
Turkish NLURegional Dialect≥ 0.8575%82%88%57%63%49%
MINA v3.7Target metNext-best model
Methodology & scope. Results were produced with Opinion AI’s internal evaluation system on synthetic and anonymized test sets (total n≈9,300 cases; no real patient data). The compared LLM-A / LLM-B / LLM-C are leading general-purpose large language models, evaluated under the same protocol and test set using their publicly available API versions as of May 2026, and anonymized for commercial reasons. Version updates may change these figures. The full methodology is described in the technical white paper.

Version-over-version progress

The bars below show how the primary metrics improved across v3.5 → v3.6 → v3.7 on a fixed test set.

ICD-10 · Top-1 Accuracy
v3.5 74%
v3.6 81%
v3.7 87%
SUT · Karar Accuracy
v3.5 82%
v3.6 86%
v3.7 91%
SUT · Madde MRR
v3.5 79%
v3.6 85%
v3.7 89%
Fraud · AUC-ROC
v3.5 86%
v3.6 89%
v3.7 93%
Türkçe NLU · Halk Dili
v3.5 82%
v3.6 88%
v3.7 93%
Safety · Klinik Tuzak Yakalama
v3.5 89%
v3.6 93%
v3.7 96%

Documents

Detailed findings, full benchmark tables and methodology are in these white papers. Because they contain competitive comparisons, they are not published publicly; they are provided to organizations on request under a confidentiality agreement (NDA).

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Evaluate MINA v3.7
in your own workflows

Through a POC, we can examine with you how MINA works in your clinical and administrative workflow.

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