Advancing AI in health care: It’s all about trust

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Advancing AI in health care: It’s all about trust

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It’s just completely obvious that within five years, deep learning is going to do better than radiologists.”
Today, hundreds of startup companies around the world are trying to apply deep learning to radiology.
There are many proofs of concept, such as automated diagnosis of pneumonia from chest X-rays, but surprisingly few cases in which deep learning (a machine learning technique that is currently the most dominant approach to AI) has achieved the transformations and improvements so often promised.
Meanwhile, a growing body of literature shows that deep learning is fundamentally vulnerable to “adversarial attacks,” and is often easily fooled by spurious associations.
For example, deep-learning algorithms trained on X-ray images to make diagnostic decisions can easily detect the imaging machine used to make the images.
Patients who are bedridden due to their conditions must be imaged at the bedside using the portable machine.
Deep-learning systems excel at finding associations within the training data, but have no ability to differentiate what is causally relevant from what is accidentally correlated, like fuzz on an imaging device.
In this way, the presence of a ruler becomes associated with a cancer diagnosis in the image data.
An AI algorithm may well leverage this association, instead of the visual appearance of the lesion, to make cancer decisions.
Deep-learning systems excel at classifying images but radiologists (and other doctors, such as pathologists) must integrate what they see in an image with other facts about patient history, currently prevalent illnesses, and the like.
In drug trials, it is a given that success in Phase 1 is no guarantee of success in Phase 3, whereas in the current mania for deep learning and AI, a preliminary proof of concept is taken seriously — but prematurely — as cause to revamp medical school curricula

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