The QZ article is narrowly correct but widely misleading. It almost willfully ignores the momentum and direction.
In reality, radiologists will not be summarily replaced one day. They will get more and more productive as tools extend their reach. This can occur even as the number of radiologists increases.
Here's a recent example where Hinton was right in concept: recent AI work for lung cancer detection made radiologists perform better in an FDA 510k clearance.
20 readers reviewed all of 232 cases using both a second-reader as well as a concurrent first reader workflows. Following the read according to both workflows, five expert radiologists reviewed all consolidated marks. The reference standard was based on reader majority (three out of five) followed by expert adjudication, as needed. As a result of the study’s truthing process, 143 cases were identified as including at least one true nodule and 89 with no true nodules. All endpoints of the analyses were satisfactorily met. These analyses demonstrated that all readers showed a significant improvement for the detection of pulmonary nodules (solid, part-solid and ground glass) with both reading workflows.
(I am proud to have worked with others on versions of the above, but do not speak for them or the approval, etc)
The AI revolution in medicine is here. That is not in dispute by most clinicians in training now, nor, from all signs, by the FDA. Not everyone is making use of it yet, and not all of it is perfect (as with radiologists - just try to get a clean training set). But the idea that machine learning/ai is overpromising is like criticizing Steve Jobs in 2008 for overpromising the iphone by saying it hasn't totally changed your life yet. Ok.
This is how it needs to be approached. AI systems and rule based systems that work together with the clinicians to enhance their decision making ability instead of replacing them.
There were limited scope CADe results showing improvements over average readers 20 years ago, and people calling it a 'revolution' then. I'm not sure anything has really shifted; the real problems in making clinical impact remain hard.
In reality, radiologists will not be summarily replaced one day. They will get more and more productive as tools extend their reach. This can occur even as the number of radiologists increases.
Here's a recent example where Hinton was right in concept: recent AI work for lung cancer detection made radiologists perform better in an FDA 510k clearance.
20 readers reviewed all of 232 cases using both a second-reader as well as a concurrent first reader workflows. Following the read according to both workflows, five expert radiologists reviewed all consolidated marks. The reference standard was based on reader majority (three out of five) followed by expert adjudication, as needed. As a result of the study’s truthing process, 143 cases were identified as including at least one true nodule and 89 with no true nodules. All endpoints of the analyses were satisfactorily met. These analyses demonstrated that all readers showed a significant improvement for the detection of pulmonary nodules (solid, part-solid and ground glass) with both reading workflows.
https://www.accessdata.fda.gov/cdrh_docs/pdf20/K203258.pdf
(I am proud to have worked with others on versions of the above, but do not speak for them or the approval, etc)
The AI revolution in medicine is here. That is not in dispute by most clinicians in training now, nor, from all signs, by the FDA. Not everyone is making use of it yet, and not all of it is perfect (as with radiologists - just try to get a clean training set). But the idea that machine learning/ai is overpromising is like criticizing Steve Jobs in 2008 for overpromising the iphone by saying it hasn't totally changed your life yet. Ok.