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Operating notes, decisions, and lessons from building Humaniza Health products with AI.
The question 'which one is better?' sounds objective, but it is usually badly framed. For people working in healthcare, the most useful choice depends less on brand and more on task type, ecosystem fit, and the level of verifiability you need.
When a headline says a model outperformed physicians on an exam, it seems to compress the entire debate into one number. The problem is that a benchmark is not clinical practice, and test performance is not the same as real-world reliability.
In healthcare, the right question is not only whether AI helps. It is also what happens to data when you paste a clinical history into an external platform. Without legal basis, governance, and minimization, the shortcut can become an ethical, institutional, and legal risk.
The difference between a physician answering from memory and a physician checking the source before answering is, in large part, the difference between a plain LLM and RAG. In healthcare, that distinction changes risk, verifiability, and practical usefulness.
LLM became the popular synonym for AI, but that hides the real question: what exactly is this system doing when it answers? Understanding the basic mechanism changes how you prompt the tool and, more importantly, how much you trust it.
When someone says 'AI thought about it,' they are usually compressing three very different things into one phrase: rules, statistical learning, and neural networks at scale. Understanding that stack changes how you evaluate any clinical or academic tool.
Language models like ChatGPT, Claude, and Gemini produce plausible text, not verified truth. That subtle distinction has heavy clinical consequences — and almost no one is telling physicians about it.
There is no shortage of content about artificial intelligence out there — and very little of it is written by someone who actually sees patients. humaniza.dev is opening a trail of posts about AI, written by physicians, for healthcare professionals, with the sole ambition of explaining it without magic and without fear.