Medical AI with Knowledge-Graph Core Anchor and RAG Answer Auditing A medical knowledge graph containing ~5,000 nodes, with medical terms organized into 7 main and 2 sub-categories: diseases, symptoms, treatments, risk factors, diagnostic tests, body p…

I am relatively new to this group and based on my limited interaction, feeling quite bit of AI sceptism and fatigue here. I expected to meet industry insiders and members who are excited about hearing new developments or ideas about AI, but its not eve…

I’ve been experimenting with a slightly different approach to AI, and I’d genuinely value feedback from people working in ML, health IT, or clinical education. Instead of scaling parameters, I built a compact medical SLM (6GB) that’s tightly coupled to…

I’ve been experimenting with a slightly different approach to medical LMs and would really value feedback from people working on ML, health IT, or clinical education. Instead of chasing more parameters, I built a ~6 GB medical SLM that’s tightly couple…

LLMs, a Race for more data centers, Nvidia chips and more model parameters, yet no LLM can understand concepts and their relationships and still limited to next token prediction. Trying to increase model parameters in each generation is akin to trying …

We built a 6 GB, fully self-contained Medical SLM that runs offline on laptops and phones, no cloud, no data leaks. It combines BioGPT-Large + a native biomedical knowledge graph (5 000+ nodes, 25 000+ edges) with graph-aware embeddings and real-time R…

I already created a medical graph dictionary with nodes and edges, generated uniform graph vectors (85%) and combined them with MiniLLM vectors (15%) and utilized successfully in MLM and CLM (preidict next token) training. With only 500 Pubmed data sam…

Below code was utilized create unform graph vectors based on nodes and edges of a medical graph dictionary with 500 nodes (body parts, cellular structure, diseases, medical treatment, symptoms), hierarchical order (parent, child) and medical relationsh…

Medical SLM Model Output based on Graph Dictionary, 85% to 100% token success, 0.002 loss, 1.01 perplexity and all of this based on only 500 PubMed dataset samples and 85% weight on graph dictionary vector embeddings, These are simply results of 20 epo…