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OpenMedLLM-70BClinical AIdiagnose(symptoms)DiagnosisMedQA: 91.2% ✓ValidatedFlagship open-source medical LLM for clinical decision support, differential diagnosis, and treatment planning. 91.2% on MedQA benchmark. Trained on 58M+ medical papers.
Biomedical literature understanding model fine-tuned on PubMed, clinical trial reports, and drug interaction databases. Excels at medical Q&A and summarization.
Specialized clinical reasoning model optimized for complex differential diagnosis chains, treatment protocol adherence checking, and clinical note summarization. HIPAA-aware architecture.
Radiology report generation and interpretation model. Generates structured reports from imaging findings, flags anomalies, and suggests follow-up recommendations using ICD-10 codes.
Drug-drug and drug-disease interaction prediction model. Trained on DrugBank, FDA adverse event data, and PharmGKB. Supports polypharmacy risk scoring.
Pathology report generation from histopathology image descriptions. Covers 18 cancer types with WHO grading, staging, and treatment pathway recommendations.
Install the OpenMedLLM Python SDK and start diagnosing, summarizing clinical notes, and generating reports immediately. Works on any GPU from A10 to H100.
from openmedllm import MedicalLLM
model = MedicalLLM("deepcog-ai/OpenMedLLM-70B")
result = model.diagnose(
symptoms="chest pain, dyspnea, diaphoresis",
patient={"age": 58, "sex": "M"}
)
print(result.differential) # Top diagnoses
print(result.urgency) # "Immediate"OpenMedLLM-70B leads on MedQA, USMLE, ClinicalBench, and PubMedQA — outperforming GPT-4 on medical reasoning tasks while remaining fully open-source.
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