Thanks. I probably should have caveated that statement with some qualifier. I'm not particularly worried about it in the short-term timeframe in which I expect to be earning my bread as a practicing surgical pathologist. I absolutely expect at some point, machine learning will have progressed to the point to be able to perform my job as well as I can. I just think that using it in a clinical setting is a long way off, and far before we get to that point, we will be using pure molecular techniques to diagnose and classify cancer. If I had to list the tasks that I would imagine machine learning to be able to do at an expert level before my eye/brain combination could be replaced for diagnosing cancer on glass slides it may look like:
- Segregate tissue from non-tissue (ie. blank slide space)
- Classify the tissue (epithelial versus mesenchymal/stromal, mucin/matrix,
exogenous material, etc).
- Decide if it is normal or abnormal
- Decide if it is native to whatever organ that it reportedly came from or is it
a metastases from somewhere else.
- Select appropriate immunohistochemical stains or other ancillary tests (eg.
molecular assays).
- Appropriately interpret the immunohistochemical stain (eg. is it a nuclear stain
or cytoplasmic, or both, or membranous).
Is that staining true staining or artifactual.
- Interpret that in the clinical context of the patient's history.
And it has to do all of those things and correct and account for bad preparations from the laboratory, inconsistent staining from day-to-day and slide-to-slide, non-representative biopsies from the surgeons etc.
I think at the end of my career, they will eventually haul my carcass off of my microscope (hopefully!) in 40 to 50 years hence. I imagine that we will have made a ton of progress with computer vision in pathology, and will be using it in an adjunct fashion. To help with analyzing results and other tedious tasks that computer vision is well-suited for. But a lot of medicine, and especially pathology, is making intuitive connections and conclusions with inconsistent and incomplete evidence in day-to-day work on individual patients.
Yeah, that's exactly the type of 'noise' I was referring to. Improper histological staining, poor imaging skills, etc. are all things that the human mind can immediately discover. The human mind is smart enough to not rely on the assumption that all random variables implicated are conditionally independent of each other. Computers, on the other hand, use algorithms rooted in these assumptions.
I think at the end of my career, they will eventually haul my carcass off of my microscope (hopefully!) in 40 to 50 years hence. I imagine that we will have made a ton of progress with computer vision in pathology, and will be using it in an adjunct fashion. To help with analyzing results and other tedious tasks that computer vision is well-suited for. But a lot of medicine, and especially pathology, is making intuitive connections and conclusions with inconsistent and incomplete evidence in day-to-day work on individual patients.