Mental-health domain LLM
Trained on 286,000 real mental-health cases, the model deeply understands psychiatric clinical language, diagnostic logic, and medication standards. It is the foundation engine behind every Zenseven Assist capability.
Why Vertical Model
Why a general-purpose LLM cannot replace a domain model
The core point
The precision and specialty fit of Zenseven Assist depend entirely on the underlying domain-specific LLM—a general-purpose model cannot replace it. The foundation model is the root of Assist’s capability; without a domain base, Assist is only a generic tool.
Model Comparison
A comparison of three model types
| Dimension | Zenseven (domain) | Baichuan (healthcare) | DeepSeek / Qwen (general) |
|---|---|---|---|
| Mental-health specialty fit | Deep specialty training | Broad healthcare coverage | General capability, no specialty depth |
| Case-data foundation | 286,000 real mental-health cases | General healthcare data | No specialty-case training |
| Clinical-task performance | Psychiatric record generation and diagnostic support | Basic medical Q&A | Prone to hallucination; not suited to clinical use |
| Deployment | On-premises hospital deployment | Partial support | Mostly cloud, with higher data-compliance risk |
Model Capabilities
Core model capabilities
Trained on 286,000 real cases
Specialized training on real mental-health cases, with a deep grasp of psychiatric clinical language, diagnostic logic, and medication standards.
On-premises hospital deployment
Data stays in the hospital, meeting healthcare data-compliance requirements and protecting patient privacy.
CAC filing
Filing No. 330106003083601240013 — compliant and verifiable.
Clinical language understanding
Deep understanding of psychiatric clinical language, diagnostic logic, and medication standards.
Application Layer
Application layer: Zenseven Assist
Clinical application capabilities driven by the underlying LLM.
Symptom recognition and risk assessment
Automatically identifies mental-health symptoms and performs stratified risk assessment.
Conversational medical-record generation
Generates structured medical-record drafts in real time from clinician–patient dialogue.
Assisted care-plan recommendations
Recommends personalized care plans based on patient characteristics and historical data.
Follow-up reminders and patient management
Automated follow-up plans and lifecycle patient management.