Most explanations of how LLMs work are written for engineers, and most simplified explanations skip the actual mechanics entirely and land on metaphor instead. Neither is much use to a physician who wants a real, working understanding, not a metaphor and not a math course.
The useful starting point is the one clinicians already have: neurons and networks. A neural network is a simplified, mathematical cousin of the thing you already understand from physiology, nodes connected by weighted signals, adjusted over time based on what worked. From there, the path to a modern LLM runs through a small number of ideas that actually matter: how a network learns from data (gradient descent), how it improves from its own mistakes (backpropagation), and how it decides what to pay attention to in a sentence (attention, the mechanism behind the "transformer" in GPT and every model like it).
Written by John M. Abrahams, MD — board-certified neurosurgeon, founder of New York Brain & Spine Surgery.
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