Medical knowledge, truly understood.
Professional medical sources behind the knowledge corpus
Joint evaluation on medical accuracy, expression quality and safety ethics
Governed high-quality samples used in fine-tuning
Highest rank on the MedBench public leaderboard (tied)
Starting from authoritative medical knowledge, we connect data construction, quality governance and dedicated evaluation into a continuously iterating loop, so the model stays close to real clinical language tasks.
A structured corpus built from more than a thousand medical textbooks, clinical guidelines and reference works, spanning internal medicine, surgery, OB/GYN, pediatrics, pharmacology, diagnostics and evidence-based medicine.
KNOWLEDGEDomain adaptation across medical question answering, clinical reasoning and text comprehension, strengthening professional semantics and multi-step inference.
REASONINGSafety and ethics are scored and filtered as an independent quality dimension, reinforcing risk awareness, careful phrasing and compliance.
SAFETYYIDA Model runs a data governance system covering generation, quality filtering and evaluation feedback. Multi-dimensional scoring, category balancing and semantic deduplication keep training data professional, reliable and safe, providing a high-quality basis for every iteration.
The 14B experimental model is tied for 10th on the MedBench public leaderboard for comprehensive Chinese medical evaluation, which covers the five capability dimensions below. Results are a research-stage snapshot recorded in the paper and do not indicate clinical validity.
Standardized phrasing for case summaries, examination reports and patient-facing explanations
GENERATIONAccurate retrieval, explanation and citation of evidence-based domain knowledge
KNOWLEDGE QAMulti-step diagnostic inference, differential analysis and explainable conclusions
REASONINGSemantic understanding, element extraction and structuring of records and reports
COMPREHENSIONRisk identification, careful phrasing and control of ethical boundaries
SAFETYOverall performance keeps improving with model scale and the volume of governed data; specific benchmark scores are disclosed in the research paper.
Built around medical professionals, the model is embedded into knowledge access, text comprehension and medical education workflows.
Search, summarization and Q&A support across hospital policies, medical knowledge and professional literature.
KNOWLEDGE ASSISTANT ↗Helps interpret and structure medical text, providing a technical basis for report reading and record quality control.
CLINICAL TEXT ↗Supports pre-visit, in-visit and post-visit communication, improving response efficiency within clear safety boundaries.
PATIENT SUPPORT ↗Supports knowledge Q&A, case walkthroughs and learning feedback, linking medical knowledge with teaching practice.
MEDICAL EDUCATION ↗