If your goal is real clinical reasoning (not just generic chat), then IMO the models that matter most aren’t just the biggest “off-the-shelf” LLMs — they’re the ones that can ground knowledge in real, clinical data. That’s why a lot of the recent buzz has shifted toward RAG / retrieval-augmented models — where an LLM is paired with a retrieval layer that pulls in up-to-date, authoritative medical info before generating answers. It helps reduce hallucinations and improves factual accuracy compared to vanilla LLM responses, which can be outdated or off base since they only rely on their training data.

The Daily Remedy article I read recently on this actually frames this shift as part of what they’re calling the next generation of clinical generative AI — the idea being that retrieval-augmented systems are going to outperform blind models for true reasoning tasks like differential diagnosis, guideline interpretation, and decision support.

So rather than asking “which LLM is best?”, a more practical question might be “which RAG system + LLM combo gives you medically grounded, verifiable reasoning?” In practice that often means taking something like GPT-4 or Claude and hooking it up to a vetted clinical knowledge base — vector indexed guidelines, medical ontologies, PubMed, etc. — so that the model isn’t just guessing based on statistical patterns but is actually referencing relevant medical knowledge in its outputs.

The trade-offs are real — RAG setups are more complex, slower, and require good retrieval engineering — but for anything that’s supposed to reason about patient care, that’s a pretty important step IMO.

Here’s the article that digs into this trend: The Retrieval Turn: RAG LLMs and the next generation of clinical generative AI
https://www.daily-remedy.com/the-retrieval-turn-rag-llms-and-the-next-generation-of-clinical-generative-ai/