Patients tell their literal story. Physicians interpret it into the most likely scenario and optimize the AI inputs.
AI explores the possibilities, ranks them, and presents evidence for next steps in diagnosis and treatment. Snce Sam published this article two days ago, AI models have improved the accuracy and consistency of outputs.
We physicians then review the AI “consultation” and decide what is best for the patient. However, no two physicians are exactly alike. In fact, there is a wide variation in test ordering and disposition decision-making based on each person’s training, experience, and “tolerance for uncertainty.“
Yes, absolutely. But you raise a good point here. The process you described sets specific guardrails on what the AI is supposed to do and how human physicians are supposed to interpret that output. Right now, those guardrails don’t exist. I can enter any text into a medical ai and get a confident, evidence backed answer that may be completely wrong. There are no instructions on HOW to ask questions, or how to structure them, what NOT to do or say. Also, as we saw above, the ask will not divulge its weaknesses- like not being able to read data I upload. And the AI’s goal is to answer at all costs. So, we find ourselves with answers like I described in this article, completely wrong and unsafe, yet beautiful in format and heavily cited. Now the burden is on us to educate every clinician on how to use the new tool. And I think that’s entirely unfair.
Interesting how even advanced medical AI models can still struggle with nuanced clinical reasoning. Benchmarks are improving fast, but real-world medicine continues to expose the gap between pattern recognition and true understanding.
Patients tell their literal story. Physicians interpret it into the most likely scenario and optimize the AI inputs.
AI explores the possibilities, ranks them, and presents evidence for next steps in diagnosis and treatment. Snce Sam published this article two days ago, AI models have improved the accuracy and consistency of outputs.
We physicians then review the AI “consultation” and decide what is best for the patient. However, no two physicians are exactly alike. In fact, there is a wide variation in test ordering and disposition decision-making based on each person’s training, experience, and “tolerance for uncertainty.“
Yes, absolutely. But you raise a good point here. The process you described sets specific guardrails on what the AI is supposed to do and how human physicians are supposed to interpret that output. Right now, those guardrails don’t exist. I can enter any text into a medical ai and get a confident, evidence backed answer that may be completely wrong. There are no instructions on HOW to ask questions, or how to structure them, what NOT to do or say. Also, as we saw above, the ask will not divulge its weaknesses- like not being able to read data I upload. And the AI’s goal is to answer at all costs. So, we find ourselves with answers like I described in this article, completely wrong and unsafe, yet beautiful in format and heavily cited. Now the burden is on us to educate every clinician on how to use the new tool. And I think that’s entirely unfair.
Interesting how even advanced medical AI models can still struggle with nuanced clinical reasoning. Benchmarks are improving fast, but real-world medicine continues to expose the gap between pattern recognition and true understanding.
Absolutely, the performance of these models certainly leaves much to be desired.