Solutions
Clinical & Research Analysis
Accelerate clinical and research workflows with AI-powered document analysis, data extraction, and knowledge retrieval — built for accuracy, scale, and domain-heavy datasets.
Talk to our ExpertsThe Problem
Research & Clinical Data Is Complex, Fragmented, and Slow to Process
Manual review, siloed datasets, and weak AI pipelines delay insights across clinical studies, medical documents, and research archives.
Unstructured medical and research data is hard to process
Clinical notes, PDFs, lab reports, and publications come in varied formats, making reliable extraction and normalization difficult with generic AI pipelines.
Generic models miss domain-specific signals
Non-specialized models often misinterpret medical terminology, tables, and study structures — reducing accuracy and trust in downstream analysis.
Validation and auditability gaps create risk
Without traceable pipelines, versioned models, and reproducible outputs, researchers struggle to validate AI-assisted findings and meet compliance needs.
The Solution
Turn Clinical Research into Actionable Intelligence
Medical Document OCR
Extract structured data from clinical PDFs, reports, forms, and scanned records into large-scale knowledge bases.
Research Paper Parsing
Automatically summarize papers into sections, tables, citations, and figures for faster access.
Domain-Tuned Models
Run models optimized for biomedical and scientific language across modalities.
RAG for Research Info
Elevate retrieval performance across journals, trial data, and internal research knowledge bases.
Batch Study Processing
Analyze thousands of documents and datasets in parallel on GPU-powered infrastructure.
Traceable AI Pipelines
Maintain logs, versioning, and reproducibility metrics for regulated and high-assurance environments.
Recommended Models
Recommended Models for Clinical & Research AI
Use high-accuracy OCR, long-context, & domain-capable models designed for complex documents & research-heavy workloads.
Released in late 2025, HunyuanOCR is an open-source contribution from Tencent that outperforms many larger proprietary models. It utilizes a "Global-to-Local" architecture with a SigLIP-v2 visual encoder to handle high-resolution inputs and extreme aspect ratios (like long receipts) without splitting images artificially.
Qwen3-VL-30B-A3-Instruct is a large-scale, high-capacity vision-language instruction model designed for advanced multimodal reasoning. It delivers significantly stronger visual understanding.
Move Clinical & Research AI from Pilot to Production the Qubrid Way
Planning large-scale research or clinical AI deployment? Work with our team to design a production-ready pipeline.
“Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements.”
Clinical AI Team
Research & Clinical Intelligence