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This model is protected under IP Application 2442 filed with the United Arab Emirates University (UAEU). Access requires institutional affiliation and is granted for non-commercial research purposes only. Commercial licensing inquiries should be directed to the UAEU Intellectual Property Unit at IP@uaeu.ac.ae
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🩺 Diagnostic-Reasoning-Q3
Pentabrid V9 — Clinical Reasoning AI
The highest-performing open-source sub-10B medical reasoning model
Developed by Dr Adnan Agha College of Medicine and Health Sciences | United Arab Emirates University Clinical-Reasoning-Hub
🎯 Overview
Diagnostic-Reasoning-Q3 represents the second generation of the Pentabrid clinical reasoning methodology — a novel approach to medical AI training that mirrors how physicians actually learn and reason. Unlike conventional medical QA models that rely on pattern matching, this system is trained using evidence-based Bayesian clinical reasoning methodology, incorporating likelihood ratios from peer-reviewed sources and structured diagnostic frameworks.
The model achieves 76.4% average across 7 major clinical benchmarks — a +24.2 percentage point gain over the Qwen3-8B base model. These results place it among the strongest medical AI models in the sub-10B parameter class globally.
✨ Key Innovations
| Innovation | Description |
|---|---|
| Pentabrid Framework | 5-phase self-correcting clinical reasoning methodology |
| Evidence-Based Reasoning | 500+ likelihood ratios extracted from clinical scenarios and textbooks |
| Diagnostic Frameworks | Structured approaches from textbooks to identify diagnosis based on presentation |
| Clinical Behavior Training | 125+ real anonymized clinical cases using clinical reasoning methodology |
| Weighted Curriculum Learning | Prevents catastrophic forgetting through strategic data mixing |
| Transparent Reasoning | Uses <think> tags for explicit step-by-step clinical analysis |
| Cross-Architecture Validation | Pentabrid methodology validated on both Qwen3 and DeepSeek-R1 bases |
📈 Headline Results
| Benchmark | Base (Qwen3-8B) | V9 Score | Improvement |
|---|---|---|---|
| Professional Medicine | 58.9% | 89.7% | +30.8pp |
| Medical Genetics | 58.6% | 88.0% | +29.4pp |
| Clinical Knowledge | 61.0% | 86.4% | +25.4pp |
| Anatomy | 57.6% | 79.3% | +21.7pp |
| MedQA (USMLE) | 43.9% | 66.3% | +22.4pp |
| PubMedQA | 48.3% | 66.6% | +18.3pp |
| MedMCQA | 37.3% | 58.6% | +21.3pp |
| Overall Average | 52.2% | 76.4% | +24.2pp |
All benchmarks evaluated using lm-evaluation-harness v0.4.11 with zero-shot log-likelihood scoring on official test splits.
🏆 Competitive Positioning
vs Published Medical AI Models (sub-10B class)
| Model | Parameters | MedQA | Prof Med | Clinical Knowledge |
|---|---|---|---|---|
| Diagnostic-Reasoning-Q3 (Ours) | 8B | 66.3% | 89.7% | 86.4% |
| MedReason-8B | 8B | 61.7% | — | — |
| OpenBioLLM-8B | 8B | 51.0% | — | — |
| Huatuo-o1-8B | 8B | ~59% | — | — |
| DeepSeek-R1-Distill-8B (base) | 8B | 52.0% | — | — |
Diagnostic-Reasoning-Q3 surpasses MedReason-8B on MedQA by +4.6pp and achieves MMLU medical scores competitive with models 4-9x larger.
📊 Training Methodology: The Pentabrid Framework
This model was trained using the Pentabrid clinical reasoning framework — a proprietary methodology that mirrors how physicians actually learn and reason, built on three evidence-based pillars.
Full methodology details are proprietary and protected under IP Application 2442. For licensing, see 💼 Commercial Licensing and Enterprise Partnerships below.
💡 Example: Bayesian Clinical Reasoning in Action
User: A 65-year-old male presents with crushing chest pain radiating to the left arm, diaphoresis, and ST elevation in leads V1-V4. What is the most likely diagnosis?
A. Stable angina B. Acute anterior STEMI C. Pericarditis D. Pulmonary embolism
Model: Analyzing this case using Bayesian clinical reasoning.
Key Findings:
65-year-old male — cardiac risk age Crushing chest pain radiating to left arm — classic ACS pattern Diaphoresis — autonomic response suggesting significant myocardial compromise ST elevation in V1-V4 — anterior leads, LAD territory Pre-test Probability: Male >55 presenting to ED with chest pain: ~30% baseline for ACS
Applying Likelihood Ratios:
Radiation to left arm: LR+ 2.3 → probability ~50% Diaphoresis: LR+ 2.0 → probability ~65% ST elevation in V1-V4: LR+ >10 → probability >95% Eliminating alternatives: A. Stable angina — does not cause ST elevation; resolves with rest C. Pericarditis — shows diffuse ST elevation, not localized to V1-V4 D. Pulmonary embolism — shows right heart strain, not anterior ST elevation
The localized anterior ST elevation with classic symptoms points to LAD territory infarction.
The answer is B. Acute anterior STEMI
Post-test probability: >95%. This patient requires immediate cardiac catheterization for primary PCI. The ST elevation in V1-V4 indicates LAD occlusion with anterior wall myocardial infarction.
🔧 Technical Specifications
| Attribute | Value |
|---|---|
| Base Model | Qwen/Qwen3-8B |
| Base Pretraining | 36 trillion tokens, 119 languages, explicit STEM reasoning stages |
| Architecture | Transformer with native reasoning support |
| Parameters | 8 Billion |
| Precision | BF16 (full precision) |
| Context Length | 4,096 tokens (training) |
| Fine-tuning Method | QLoRA (rank 128, alpha 256) |
| Training Hardware | NVIDIA H100 80GB |
| Training Duration | 16 hours (main) + 0.3 hours (stabilizer) |
| Training Data | ~92,000 curated examples (composition proprietary) |
| Evaluation | lm-evaluation-harness v0.4.11 |
🔀 Parallel Architecture: V8 (DeepSeek-R1 Base)
The identical Pentabrid training pipeline applied to DeepSeek-R1-Distill-Llama-8B produces V8 at 64.5% average — confirming the methodology transfers across architectures. The Qwen3-8B base stronger pretraining foundation (36T vs 15T tokens) acts as a multiplier on training effectiveness.
🔒 Intellectual Property and Access
IP Protection Status
This model, its training methodology, and associated datasets are protected intellectual property.
IP Application 2442 Filed with the United Arab Emirates University (UAEU) Research and Sponsored Projects Office Intellectual Property Unit
All rights reserved. Unauthorized commercial use, reproduction of methodology, or derivative works are prohibited under the CC-BY-NC-ND-4.0 license and applicable intellectual property law.
Requirements for Access
| Requirement | |
|---|---|
| ✅ | Verifiable real name |
| ✅ | Institutional affiliation (university, hospital, research organization) |
| ✅ | Clear research purpose with specific use case |
| ✅ | Agreement to non-commercial, research-only use |
| ✅ | Acknowledgment of UAEU IP protection |
Requests that will be declined
| Reason | |
|---|---|
| ❌ | Anonymous accounts |
| ❌ | Vague or missing research justification |
| ❌ | Commercial deployment intentions |
| ❌ | Clinical decision-making applications |
🏛️ Institutional Details
United Arab Emirates University College of Medicine and Health Sciences Al Ain, Abu Dhabi, UAE
IP Contact:
- Email: IP@uaeu.ac.ae
- Phone: +971 3 713 4035 / 5848
- Location: Crescent Building D4, First Floor
- UAEU IP and Patents Office
- Research Office
💼 Commercial Licensing and Enterprise Partnerships
We welcome inquiries from healthcare technology companies, pharmaceutical organizations, hospital systems, and EHR vendors interested in deploying clinical reasoning AI at scale.
What We Offer
| Offering | Description |
|---|---|
| Technology Licensing | License the Pentabrid methodology and trained models for commercial deployment |
| Custom Fine-Tuning | Specialty-specific models (cardiology, oncology, emergency medicine, etc) |
| Enterprise Integration | API deployment, EHR integration, clinical decision support systems |
| Research Collaboration | Joint development with academic medical centers and health tech companies |
| White-Label Solutions | Branded clinical reasoning AI for your platform |
Commercial Contact
All commercial licensing, partnership, and acquisition inquiries should be directed to:
UAEU Intellectual Property Unit Research and Sponsored Projects Office United Arab Emirates University
- Email: IP@uaeu.ac.ae
- Phone: +971 3 713 4035 / 5848
- Web: https://www.uaeu.ac.ae/en/research/research-and-sponsored-projects-office/intellectual-property.shtml
Or contact the developer via UAEU website helpdesk uaeu.ac.ae :
- Dr Adnan Agha, College of Medicine and Health Sciences, United Arab Emirates University
⚠️ Intended Use and Safety
✅ Intended Use
| Use Case | Description |
|---|---|
| Clinical Research | Analyzing patterns in de-identified medical data |
| Medical Education | Simulating diagnostic scenarios for training |
| AI Benchmarking | Evaluating against other medical reasoning systems |
| Methodology Research | Studying Bayesian reasoning in AI systems |
❌ Limitations & Warnings
⚠️ NOT A MEDICAL DEVICE — This model is NOT a licensed medical professional and must NOT be used to provide medical advice, diagnosis, or treatment to real patients.
| Limitation | Description |
|---|---|
| Hallucination Risk | Like all LLMs, outputs may contain fabricated information |
| No Real-Time Knowledge | Training data has a cutoff date |
| Bias | May reflect biases present in medical literature |
| Validation Required | All outputs must be verified by qualified professionals |
| Not FDA/CE Approved | Not cleared for clinical or diagnostic use |
| No Derivative Works | Must not be used to create derivative models (ND license term) |
📖 Citation
@misc{agha2025diagnosticreasoningq3,
author = {Agha, Adnan},
title = {Diagnostic-Reasoning-Q3: Pentabrid Clinical Reasoning Model},
year = {2025},
publisher = {Hugging Face},
institution = {United Arab Emirates University, College of Medicine \& Health Sciences},
howpublished = {\url{https://huggingface.co/Clinical-Reasoning-Hub/Diagnostic-Reasoning-Q3X1}},
note = {IP Application \#2442, UAEU. Fine-tuned from Qwen3-8B using
evidence-based Pentabrid clinical reasoning methodology.}
}
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