Neuro-symbolic AI

A hybrid approach that puts the pattern recognition of neural networks together with the explicit rules and logic of symbolic AI. The neural side is good at perception and bad at guarantees. The symbolic side can enforce a constraint and show its working but cannot learn from raw images. Combining them is one of the more plausible routes to a system that is both accurate and checkable, which is what a high-stakes medical decision needs. Most of this is still research, though the pattern already appears in production wherever a model's output passes through a rule engine before anyone sees it. Worth knowing because it is where many people point when asked how AI becomes auditable enough for regulated clinical use.

You already use a weak version of this whenever a model's output passes a hard rule before it reaches a screen: a dose outside a permitted range is blocked, a flag on a patient who does not meet the criteria is suppressed. The learned part proposes, the rules refuse. That is the shape most regulated clinical AI settles into, because the rule layer is the part you can write down, test, and defend in a review.

Terms like this come up in real clinical scenarios across the HelloAI courses: bite-sized modules with verifiable certificates. An account takes one minute, no password needed.