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Searching for abnormal chest x-rays
The advances in AI-powered medical imaging analysis are undeniable.
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Therefore, they will only be useful in cases where the radiologist knows what to look for.
But radiologists dont necessarily start by looking for a specific disease.
And building a system that can detect every possible disease is extremely difficult if not impossible.
Defining the problem domain for deep learning systems is an act of finding the balance between specificity and generalizability.
but cant solve more specific problems.
And more importantly, it is important that a trained model can maintain its accuracy across different populations.
The deep learning model was trained on more than 250,000 x-ray scans originating from five hospitals in India.
The examples were labeled as normal or abnormal based on information extracted from the outcome report.
The accuracy of the labels in the dataset were independently reviewed and confirmed by three radiologists.
The researchers have made the labels publicly available to help future research on deep learning models for radiology.
Augmenting radiologist with deep learning
Radiology has had a rocky history with deep learning.
People should stop training radiologists now.
But five years later, AI is not anywhere close to driving radiologists out of their jobs.
And a radiologists job involvesa lot more than looking at x-ray scans.
you could read the original articlehere.
