“I think that if you work as a radiologist you are like Wile E. Coyote in the cartoon. You’re already over the edge of the cliff, but you haven’t yet looked down. There’s no ground underneath.” Deep-learning systems for breast and heart imaging have already been developed commercially. “It’s just completely obvious that in 5 years deep learning is going to do better than radiologists. It might be 10 years. I said this at a hospital. It did not go down too well.”

2022-10-05: The deep learning dividend for medicine

Today’s report on AI of retinal vessel images to help predict the risk of heart attack and stroke, from 65k UK Biobank participants, reinforces a growing body of evidence that deep neural networks can be trained to “interpret” medical images far beyond what was anticipated. Add that finding to last week’s multinational study of deep learning of retinal photos to detect Alzheimer’s disease with good accuracy. AI models have been shown to be quite useful for detecting eye diseases, such as diabetic retinopathy. But this is about the indirects, the not so obvious. That work has now extended to detection of kidney disease, control of blood glucose and blood pressure, hepatobiliary disease, a previous study on predicting heart attack, close correlation of the retinal vessels with the heart (coronary) artery calcium score

2023-07-31: Misdiagnosis is one of the biggest causes of death, yet doctors think they’re better than AI

~800k Americans are permanently disabled or die each year from diagnostic medical errors. “Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI.”

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