Med-SEAL V1 Technical Report

Version: 1.0
Date: March 2026
Status: Model trained — not deployed in demo (no GPU infrastructure)

1. What is Med-SEAL V1?

Med-SEAL V1 refers to aagdeyogipramana/Med-SEAL-V1, a fine-tuned clinical large language model based on Qwen3-VL-8B-Thinking. It is the foundational AI model purpose-built for the Med-SEAL healthcare platform.

Med-SEAL V1 is purely the base model — not the multi-agent system, not the orchestrator, and not the patient/clinician application stack. The broader Med-SEAL agent architecture (A1–A6, SEA-LION Guard, smolagents) consumes this model as its clinical reasoning backbone.

Note: Med-SEAL V1 is not yet deployed in the live system due to the absence of GPU inference infrastructure. In the current deployment, its role is fulfilled by SEA-LION v4-32B (AI Singapore) using the same prompt architecture and FHIR context pipeline. Switching to Med-SEAL V1 in production requires only environment variable changes — no code changes.

2. Model Card

Field

Value

Model name

aagdeyogipramana/Med-SEAL-V1

Base model

Qwen3-VL-8B-Thinking

Parameters

~8 billion

Modality

Vision-Language (text + image)

Fine-tuning method

Supervised fine-tuning (SFT) on clinical reasoning tasks

Domain

Chronic disease management (diabetes, hypertension, hyperlipidemia)

Region focus

Singapore and Southeast Asia

Languages

English, Chinese (Simplified), Malay, Tamil

Thinking mode

Chain-of-thought reasoning enabled

Temperature

0.3 (clinical precision)

Max output tokens

1024–2048

Serving framework

vLLM

GPU requirement

2× NVIDIA H200

API compatibility

OpenAI-compatible (/v1/chat/completions)

3. Why Qwen3-VL-8B?

The base model was selected for:

  • Vision-Language capability — can process medical images (lab reports, medication labels, wound photos) alongside text, enabling future multimodal clinical features

  • 8B parameter sweet spot — large enough for clinical reasoning quality, small enough to serve on 2× H200 GPUs with acceptable latency

  • Thinking mode — built-in chain-of-thought reasoning produces transparent, auditable clinical reasoning traces

  • Multilingual foundation — strong performance across English, Chinese, Malay, and Tamil — the four official languages of Singapore

  • Open weights — allows fine-tuning, on-premise deployment, and compliance with healthcare data sovereignty requirements

4. Fine-Tuning: From Qwen3-VL to Med-SEAL-V1

4.1 Training Objective

Transform the general-purpose Qwen3-VL-8B into a clinical reasoning specialist that can:

  1. Synthesise patient health records (FHIR-formatted) into evidence-based clinical assessments

  2. Check drug–drug and drug–food interactions

  3. Interpret lab results and vital sign trends over time

  4. Generate structured clinical summaries for clinician pre-visit briefs

  5. Reason about multi-condition patients (diabetes + hypertension + hyperlipidemia)

  6. Produce outputs in a structured JSON format with explicit evidence citations

4.2 Training Data Domains

Domain

Content

Clinical Q&A

Drug interactions, condition explanations, lab interpretation

FHIR-grounded reasoning

Responses that cite specific FHIR resources (Observation, Condition, MedicationRequest)

Southeast Asian clinical context

Singapore healthcare protocols, local medication names, cultural health practices

Structured output

JSON-formatted clinical responses with assessment, evidence, confidence, warnings

Safety alignment

Refusals for diagnosis, medication changes, emergency triage

4.3 Output Format

Med-SEAL-V1 is trained to produce structured JSON clinical responses:

{
  "assessment": "Plain-text clinical summary...",
  "evidence": [
    {
      "resource_type": "Observation",
      "resource_id": "obs-001",
      "key_value": "HbA1c 7.2%",
      "date": "2026-02-15"
    }
  ],
  "confidence": "high | medium | low",
  "warnings": ["Any safety-relevant concerns"],
  "suggested_actions": ["Optional clinician follow-ups"]
}

  • All evidence entries cite real FHIR resource IDs and dates

  • Never fabricates data not present in the provided context

  • States confidence level based on data completeness: high (clear evidence), medium (partial data), low (insufficient data)

5. Model Serving Configuration

5.1 Production Endpoint (Target)

# Production endpoint (target — self-hosted vLLM)
MEDSEAL_VLLM_URL=http://medseal-vlm.internal:8000/v1
MEDSEAL_VLLM_MODEL=aagdeyogipramana/Med-SEAL-V1
MEDSEAL_CLINICAL_LLM_BACKEND=vllm

Served via vLLM on 2× NVIDIA H200 GPUs, exposed as an OpenAI-compatible API.

5.2 Current Production Substitute

# SEA-LION v4-32B (AI Singapore)
MEDSEAL_SEALION_API_URL=https://api.sea-lion.ai/v1
MEDSEAL_SEALION_API_KEY=<your-key>
MEDSEAL_SEALION_MODEL=aisingapore/Qwen-SEA-LION-v4-32B-IT

Since Med-SEAL V1 requires dedicated GPU infrastructure, SEA-LION v4-32B (AI Singapore’s National LLM) is used as the production substitute. The same prompt templates, FHIR context injection, and safety guards are applied regardless of which model is behind the endpoint.

5.3 Infrastructure Requirements

Resource

Requirement

GPU

2× NVIDIA H200 (80 GB HBM3 each)

Serving framework

vLLM

Model weights storage

~16 GB (FP16)

Endpoint

Private, OpenAI-compatible /v1/chat/completions

Latency target

< 10 seconds for clinical reasoning responses

6. How the Agent System Uses Med-SEAL V1

Med-SEAL V1 is consumed by the broader agent architecture as follows:

Patient / Clinician
        │
  SEA-LION Guard (input)
        │
  smolagents Orchestrator
        │
   ┌────┴─────┐
   │ A2 Agent │  ←── calls Med-SEAL-V1 for clinical reasoning
   │ A5 Agent │  ←── calls Med-SEAL-V1 for pre-visit briefs
   └────┬─────┘
        │
  ┌─────▼──────┐
  │ Med-SEAL-V1│  ← THIS IS MED-SEAL V1
  │  (LLM)     │  ← huggingface.co/aagdeyogipramana/Med-SEAL-V1
  │  (LLM)     │
  └─────┬──────┘
        │
  SEA-LION Guard (output)
        │
  FHIR R4 response

  • A2 (Clinical Reasoning Agent) — routes clinical questions (drug interactions, lab interpretation, condition reasoning) to Med-SEAL-V1

  • A5 (Insight Synthesis Agent) — uses Med-SEAL-V1 to generate pre-visit clinical briefs from aggregated patient data

  • A1 (Companion Agent) — indirectly uses Med-SEAL-V1 via delegation to A2 for complex medical questions, then rephrases the response for patients

Other agents (A3 Nudge, A4 Lifestyle) use different models (MERaLiON, SEA-LION) and do not depend on Med-SEAL V1.

7. Clinical Capabilities of Med-SEAL-V1

7.1 Drug Interaction Checking

  • Input: List of active medications (RxNorm-coded from FHIR MedicationRequest)

  • Output: Interaction severity, affected drug pairs, clinical recommendation

  • Checks both drug–drug and drug–food interactions

7.2 Lab Result Interpretation

  • Input: FHIR Observation resources (LOINC-coded) with temporal data

  • Output: Trend analysis across ≥ 3 data points over 90+ days

  • Covers: HbA1c, blood pressure, fasting glucose, lipid panel, eGFR

7.3 Multi-Condition Clinical Reasoning

  • Reasons across co-morbid conditions: Type 2 diabetes + hypertension + hyperlipidemia

  • Identifies interactions between conditions and their respective treatment plans

  • Produces holistic clinical assessments rather than single-condition responses

7.4 Pre-Visit Brief Generation

  • Aggregates 30 days of patient-side data (adherence, biometrics, PRO scores, engagement)

  • Outputs structured FHIR Composition sections

  • Designed for delivery to clinicians via CDS Hooks ≥ 30 min before appointment

7.5 Safety Constraints (Built into the Model)

  • Never fabricates clinical data not present in the provided FHIR context

  • Never recommends starting, stopping, or changing medications

  • Never provides a diagnosis — only explains existing conditions on record

  • Never triages emergencies — defers to clinical escalation pathways

  • All outputs pass through the external SEA-LION Guard before reaching any user surface

8. Deployment Status

Med-SEAL V1 (aagdeyogipramana/Med-SEAL-V1) requires 2× NVIDIA H200 GPUs running vLLM to serve the model at production-grade latency (< 10s per request). In the current deployment, SEA-LION v4-32B serves as the clinical reasoning backend.

What the current deployment preserves without Med-SEAL V1:

Aspect

Preserved?

Detail

Prompt architecture

✅ Yes

Same system prompts, few-shot examples, instruction templates

FHIR context injection

✅ Yes

Same Patient, Condition, Observation, MedicationRequest loading

Safety guards

✅ Yes

SEA-LION Guard input/output gates applied identically

Structured JSON output

✅ Yes

SEA-LION follows the same output schema

Clinical fine-tuning quality

❌ No

SEA-LION lacks domain-specific fine-tuning for clinical reasoning

Multimodal (image) support

❌ No

SEA-LION does not replicate Med-SEAL-V1’s vision-language capabilities

Latency guarantees

✅ Yes

SEA-LION API provides consistent latency

To switch to Med-SEAL V1: update MEDSEAL_CLINICAL_LLM_BACKEND=vllm and set the vLLM endpoint. No code changes required.

9. Relationship to V2

Med-SEAL V1 is the first-generation model. In the V2 roadmap, the model may be updated or replaced, but V1 remains the baseline:

V1

V2 (Planned)

Single model (Med-SEAL-V1) serves all clinical reasoning

Potentially specialised models per agent

Qwen3-VL-8B base

May upgrade to larger or more specialised base

2× H200 serving

Scaled serving with load balancing

SFT-only training

May add RLHF or DPO alignment stages

10. References