Raw LLM Responses

The point they were trying to make is that AI's ability to pass the test with flying colors is not a reflection of the ability to treat patients. - the right answer is not always a diagnosis, it might be a "what's the next best step in this scenario" when in reality you would be doing a combination of all the things in the options since they all need to happen. AI might get it right because it remembers the algorithm better and knows step 5 is listed before 6,7 and 8. Some questions omit pertinent data that in real life would make it clear that step 5 comes next but without that data a physician may need to look up the algorithm quickly to know it's 5-6-7-8 when in reality all the steps will be happening together. Time is not a real thing in questions, things that would take hours/days/months are just the next question stem away. Curveballs/confounding factors/diagnostic dilemmas/indeterminate or borderline test results/decompensating patients while results are pending are not things that are easily testable. - getting confused between the book answer and insurance related issues is just a matter of a human jumping around stupid loops to provide as close as effective care and not being able to turn off that mindset on the test day. Doesn't mean much except for the fact that the person is not a good test taker. What the AI lacks(at least for now) is nuance. Having said all of this, AI is going to be exciting to have as a tool. It's great to use an AI as a refresher of the latest guideline updates or what the latest evidence is for a treatment. It will likely also bring a cutting edge to medical care with application of high quality evidence based care. It would certainly help streamline and standardize care across regions and hospitals, eliminate "institution specific practices" and things rooted in tradition that have no evidence. Medical errors will likely be reduced. Future medical study designs and statistical analysis will improve on quality. It may als