tags:
- medical LLM，R&D，Life science
CrossNow AI
LLMs for medical
We built upon a life‑science knowledge framework, integrates hospital data (e.g., de‑identified medical records, clinical guidelines), pharmaceutical information (e.g., drug development, clinical trials, package inserts), and regulatory compliance data. Its core lies in constructing a “compliance‑aware brain” that combines a medical knowledge graph (millions of entities) with retrieval‑augmented generation (RAG) and fine‑tuning techniques.
Key features:
Data foundation: High‑quality, curated, and anonymised data from clinical practice and drug development, annotated by medical experts to avoid “hallucinations” and low‑quality web data.
Compliance pillars:
Data compliance – rigorous de‑identification and standardisation.
Algorithmic compliance – guardrails, real‑time risk interception, and pharmacovigilance monitoring.
Operational compliance – AI supports decision‑making, but final clinical actions (e.g., prescriptions) remain with human professionals.
Four operational modules:
Data perception – multimodal processing (text, images, molecular structures). Knowledge enhancement – secure, auditable integration of R&D assets. Logical reasoning – AI agents bridging healthcare, insurance, and pharmacy workflows. Intelligent interaction – natural language Q&A with traceable citations. Application scenarios:
Healthcare – assisted diagnosis, personalised treatment, medication safety.
Pharma R&D – target discovery, clinical trial optimisation, adverse event monitoring.
Medical insurance – automated fraud detection and cost control.
Regulation – compliance reporting, intelligent audits.
In essence, the model acts as an intelligent hub across the full life‑science chain – diagnosis, medication, R&D, payment, and regulation – driving efficiency and precision medicine while upholding strict safety and compliance standards.
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Focus areas
medical LLM，R&D，Life science