4 ms·

A few thoughts below as a medical student with some ML background. I'm quite interested in hearing what others think: 1. The majority of clinical ML application

A few thoughts below as a medical student with some ML background. I'm quite interested in hearing what others think: 1. The majority of clinical ML applications are still focused on narrow (e.g. binary) image recognition tasks. This makes sense because it's where CNN's excel. Also, many of these tasks are well reimbursed. I expect the performance to continue improving as datasets and annotations improve. 2. There is less activity on broad image recognition tasks, like reading a CT Abdomen and Pelvis. Here the domain of possible diagnoses is rather large. For example, patients sometimes have unusual anatomy from prior surgeries. Or, a cancer may obliterate common anatomical landmarks. An experienced radiologist or surgeon can figure out this anatomy in a few minutes, whereas ML may need new approaches to get to this level. 3. The history and physical exam are clinician-generated, not just for dataset annotations but for every new patient. This is notable because the history and physical exam determine the majority of what a patient needs. It is not only a matter of knowing which questions to ask, it also requires follow-up questions and a degree of mental processing before findings are written down in a note. A lot of patients will not fully cooperate without patience and empathy. So "AI" replacing doctors actually trend perilously close to AGI. This is both interesting, and difficult. Of course, NLP of human-generated notes / unstructured data is a very interesting area, but it still requires a human in the loop.

chispamed 6y ago
As someone with your exact background, I agree with you on almost all points. However I think that the common notion is less that AI will/should replace physicians but rather that AI could help physician to free time that they could then spend with patients. So the ongoing human involvement is an almost explicit goal (outside of radiology and pathology at least). On another note and speaking from personal experience, NLP as well as patient information and note processing is something that might be less glorious than AI analysis of MRIs for example but which would have a far greater impact on physicians’ day to day life. Most residents spend like 50% of their time fighting applications with some of the worst UX I have ever seen just to get some basic information, log something or schedule a simple procedure.
abbirdboy 6y ago
Another medical student here, also with a very similar ML/programming background. Like the other commenter, I couldn't agree more with your analysis of the main issues plaguing current DL applications in medicine. When I was working on deep learning projects as a undergrad before coming to medical school, I naively assumed that solving a simple image classification problem for cancer detection would be enough. However, as one learns in medical school, imaging is only one component of an entire clinical vignette. Even before a patient undergoes a specific test, the history and physical exam really drive the initial protocols. Sometimes, without having this background knowledge, the classification from a simple program has no utility. A radiologist or pathologist typically has access to this information and can interpret this in the context of the clinical suspicion being put forth. I still believe AI has a role to play in easing this workflow, but "replacing" physicians will take a lot more than detecting some disease "better" or more "accurately" than a physician.

chispamed 6y ago

As someone with your exact background, I agree with you on almost all points. However I think that the common notion is less that AI will/should replace physicians but rather that AI could help physician to free time that they could then spend with patients. So the ongoing human involvement is an almost explicit goal (outside of radiology and pathology at least). On another note and speaking from personal experience, NLP as well as patient information and note processing is something that might be less glorious than AI analysis of MRIs for example but which would have a far greater impact on physicians’ day to day life. Most residents spend like 50% of their time fighting applications with some of the worst UX I have ever seen just to get some basic information, log something or schedule a simple procedure.

abbirdboy 6y ago

Another medical student here, also with a very similar ML/programming background. Like the other commenter, I couldn't agree more with your analysis of the main issues plaguing current DL applications in medicine. When I was working on deep learning projects as a undergrad before coming to medical school, I naively assumed that solving a simple image classification problem for cancer detection would be enough. However, as one learns in medical school, imaging is only one component of an entire clinical vignette. Even before a patient undergoes a specific test, the history and physical exam really drive the initial protocols. Sometimes, without having this background knowledge, the classification from a simple program has no utility. A radiologist or pathologist typically has access to this information and can interpret this in the context of the clinical suspicion being put forth. I still believe AI has a role to play in easing this workflow, but "replacing" physicians will take a lot more than detecting some disease "better" or more "accurately" than a physician.