DeepCog.ai Blog
Deep dives into medical AI, research breakthroughs, product thinking, and the science behind our models — written by the DeepCog team.
How We Applied DPO to Fine-Tune Medical LLMs Without Losing Clinical Accuracy
Direct Preference Optimization changed how we align our models to expert clinical judgment. This post walks through our methodology, the datasets we built, and the benchmark improvements we saw across MedQA, PubMedQA, and USMLE.
Genomic Variant Interpretation at Scale: How GenomicLLM-7B Approaches VCF Classification
A technical overview of how our GenomicLLM-7B model processes variant call format files, applies ACMG 2015 criteria, and generates ClinVar-compatible interpretation reports — with 97.4% concordance to expert panels.
What Clinicians Actually Need from AI: Lessons from 18 Months of Hospital Deployments
After deploying ClinicalReasoner-13B across three health systems, here's what we learned about the gap between what AI can do and what clinicians actually need at the bedside.
Building the DeepCog Platform: Why We Chose a Layered Architecture for Clinical AI
We made some unconventional decisions when designing the platform stack. This post explains why we separated the inference, data, and security layers — and what it means for deployment flexibility.
RAG vs Fine-Tuning for Medical Q&A: A Head-to-Head Benchmark on OpenBioLLM
We ran extensive experiments comparing retrieval-augmented generation against domain-specific fine-tuning for medical question answering. The results surprised us — and the winner depends heavily on your use case.
Why We Started DeepCog.ai: The Case for Specialized Medical Intelligence
General-purpose LLMs aren't good enough for medicine. Here's the founding story behind Deep Cognition Labs and why we believe domain-specialized AI is the only responsible path forward for healthcare.