One of the challenges of medicine is that the information is gathered from so many sources and is so "fuzzy" in quality.

Building a "database" of information from which to make a diagnosis is unlikely to be easily automated. Take a straightforward case of a patient who comes to the emergency department after "fainting". Did they slowly kind of "melt" to the ground, or did they just BOOM fall? Were they confused after they woke up, or just a little sleepy? Was it a hot day or is it wintertime? Were they wearing a shirt and tie, or a t-shirt? Different answers to each of these questions will change the probability of each potential diagnosis. The signal:noise ratio is frequently very low, and there's not a great way to improve it without adding an extremely large amount of cost and time to an already expensive and slow healthcare system.

Good clinicians already have an idea of the top 2-3 most likely possibilities before they walk into a patient's room, based on epidemiology and a quick review of a patient's chart, but we try to be flexible enough to discard those preconceptions if new info becomes available. Sometimes clinicians fail to fully investigate what a patient is telling them, and that's where the real mistakes get made.