that's a horrible idea. careless use of machine learning in medicine is one of the few legitimate "AI safety" cases worthy of concern. ML is far too unreliable and doctors too illiterate for medical imagery diagnostics to be safe without an extremely careful approach.
unfortunately, the cat's out of the bag and what is currently in use in hospitals around the world is far scarier than misuse of diagnostics. GE has irresponsibly deployed a ML based reconstruction method [1], which freed from the onerous constraint of having to be correct, makes much crisper images than classical methods. classical compressed sensing reconstruction methods come with a mathematical guarantee on the fidelity of the reconstruction. GE's approach does not, it's just a supervised method. kid stuff. the FDA, for their part, is also illiterate and rubber-stamped the method after looking at their paper where 9/10 radiologists preferred the ML reconstructed images.
would be remiss to not point out that Ajil Jalal et al. are doing great work on reconstruction guarantees for compressed sensing with learned priors
> ML is far too unreliable and doctors too illiterate for medical imagery diagnostics to be safe without an extremely careful approach.
"Safe" isn't always the only objective. In some cases access to "unsafe" medical AI is better than nothing.
Your mindset is shaped by living in a wealthy country. Do you know much about the medical systems of countries with $1000 GDP per capita? Most poor people in these countries don't have access to a doctor. The wait times in public are so long and cumbersome (and still cost an arm and a leg) that they don't even bother going. Then they die. It's not the country's fault, that's just the reality of poverty, there aren't enough trained doctors.
The only honest comparison therefore is not always "doctor vs AI" it's "nothing vs AI".
unfortunately, the cat's out of the bag and what is currently in use in hospitals around the world is far scarier than misuse of diagnostics. GE has irresponsibly deployed a ML based reconstruction method [1], which freed from the onerous constraint of having to be correct, makes much crisper images than classical methods. classical compressed sensing reconstruction methods come with a mathematical guarantee on the fidelity of the reconstruction. GE's approach does not, it's just a supervised method. kid stuff. the FDA, for their part, is also illiterate and rubber-stamped the method after looking at their paper where 9/10 radiologists preferred the ML reconstructed images.
would be remiss to not point out that Ajil Jalal et al. are doing great work on reconstruction guarantees for compressed sensing with learned priors
[1] https://www.gehealthcare.com/products/magnetic-resonance-ima...