- Joined
- Oct 28, 2013
- Messages
- 73
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- 14
I'm really enjoying this discussion, for the record. And I don't mean to gang up on the only person avidly defending AI, but I can't resist picking at an argument or two.
I appreciate you addressing the EKG issue, Naijaba, but am unconvinced by your argument that EKGs are less lucrative than chest radiographs. The work RVUs generated for the professional component of both studies are almost equivalent (0.15 for EKG, 0.18 for CXR amounting to roughly $5.40 and $6.50 a pop at the 2017 conversion rate, not accounting for geographic variability). I would argue that more EKGs are performed and interpreted day-to-day than CXRs. And if you're going to make and the argument that value-based care will result in decreased reimbursement for CXRs due to the supposedly low clinical utility of a radiologist's interpretation, then I really don't understand how one could make the argument that CXR-reading AI is more lucrative than EKG-reading AI.
As to the claim that neurosurgery residents think they come out of residency reading neuroimaging studies as well as a fellowship-trained neuroradiologist, I wholeheartedly disagree with that idea and think that those surgeons are deluding themselves and putting their patients at risk. Knowing how to look at a tumor on a brain MR from the surgeon's perspective is very different from looking at a brain MR performed with the tumor protocol from the radiologist's perspective. For one, surgeons hate dealing with incidental findings when they don't read the study. How do you think they'd feel if now they were not only responsible for handling the follow-up and management of such findings, but also for detecting them in the first place? But also, I can tell you from personal experience, that even cardiologists trained in cardiac imaging struggle with detecting incidental findings on cardiac MR, even though they understand cardiac physiology better than radiologists and, as a result, may do a better job of interpreting the cardiac portion of the exam. Neurosurgeons are no better, though many are convinced otherwise. I've seen several cases where the surgeon makes a grave error by assuming he/she understood the imaging well enough to act. But on the other hand, I learn a lot from them by discussing how they look at different pathologies in planning their operative approach. Working together, we can use our own strengths to help each other and do the best by our patients.
Now, reverting to the neural networks vs. neuronal processing issue... Your analogy does not hold up as well as you seem to think. Transistors and neurons behave very differently. First of all, it is a gross oversimplification to say that neurons are electrical switches controlled by electricity. For one, they are not purely electrical (as is assumed by the cable model I mentioned above), but rather their conduction of signals occurs by both electrochemical and molecular biological means. Nor are they purely switches. When an action potential is generated along a neuron's axon, there are many other factors that determine the strength of the neurotransmitter response by the presynaptic neuron with as many factors determining the postsynaptic neuron's response to the neurotransmitter release generated. Whereas, a transistor in a logic gate generates an essentially binary output (allowing that even this is somewhat idealized). Put enough transistors and logic gates together and the resulting computer can estimate the output of a neuron with reasonable accuracy... However, to use those results as an argument in support of the assertion that neurons and transistors are the same is like saying that my analytical solution of a definite integral is the same as my calculator's numerical solution. Sure, the results are nearly indistinguishable, but the methods are entirely different. Also, my brain will generate the analytic solution much faster than my calculator could. And my calculator will generate the numerical solution even faster, while my brain struggles to crunch the numbers for the numerical solution.
I appreciate you addressing the EKG issue, Naijaba, but am unconvinced by your argument that EKGs are less lucrative than chest radiographs. The work RVUs generated for the professional component of both studies are almost equivalent (0.15 for EKG, 0.18 for CXR amounting to roughly $5.40 and $6.50 a pop at the 2017 conversion rate, not accounting for geographic variability). I would argue that more EKGs are performed and interpreted day-to-day than CXRs. And if you're going to make and the argument that value-based care will result in decreased reimbursement for CXRs due to the supposedly low clinical utility of a radiologist's interpretation, then I really don't understand how one could make the argument that CXR-reading AI is more lucrative than EKG-reading AI.
As to the claim that neurosurgery residents think they come out of residency reading neuroimaging studies as well as a fellowship-trained neuroradiologist, I wholeheartedly disagree with that idea and think that those surgeons are deluding themselves and putting their patients at risk. Knowing how to look at a tumor on a brain MR from the surgeon's perspective is very different from looking at a brain MR performed with the tumor protocol from the radiologist's perspective. For one, surgeons hate dealing with incidental findings when they don't read the study. How do you think they'd feel if now they were not only responsible for handling the follow-up and management of such findings, but also for detecting them in the first place? But also, I can tell you from personal experience, that even cardiologists trained in cardiac imaging struggle with detecting incidental findings on cardiac MR, even though they understand cardiac physiology better than radiologists and, as a result, may do a better job of interpreting the cardiac portion of the exam. Neurosurgeons are no better, though many are convinced otherwise. I've seen several cases where the surgeon makes a grave error by assuming he/she understood the imaging well enough to act. But on the other hand, I learn a lot from them by discussing how they look at different pathologies in planning their operative approach. Working together, we can use our own strengths to help each other and do the best by our patients.
Now, reverting to the neural networks vs. neuronal processing issue... Your analogy does not hold up as well as you seem to think. Transistors and neurons behave very differently. First of all, it is a gross oversimplification to say that neurons are electrical switches controlled by electricity. For one, they are not purely electrical (as is assumed by the cable model I mentioned above), but rather their conduction of signals occurs by both electrochemical and molecular biological means. Nor are they purely switches. When an action potential is generated along a neuron's axon, there are many other factors that determine the strength of the neurotransmitter response by the presynaptic neuron with as many factors determining the postsynaptic neuron's response to the neurotransmitter release generated. Whereas, a transistor in a logic gate generates an essentially binary output (allowing that even this is somewhat idealized). Put enough transistors and logic gates together and the resulting computer can estimate the output of a neuron with reasonable accuracy... However, to use those results as an argument in support of the assertion that neurons and transistors are the same is like saying that my analytical solution of a definite integral is the same as my calculator's numerical solution. Sure, the results are nearly indistinguishable, but the methods are entirely different. Also, my brain will generate the analytic solution much faster than my calculator could. And my calculator will generate the numerical solution even faster, while my brain struggles to crunch the numbers for the numerical solution.