Radiology And Machine Learning

Today I spent the majority of my time speaking to a prestigious world renown radiologist.

He happens to be dual board certified, fellowship trained, has multiple decades of experience in reading images, curating information that makes up a patient’s medical history, has an amazing “sense” for what is relevant versus not relevant, and is a great teacher.

My job is to translate what he does naturally into something that a computer can understand.

After 9 hours in a room, multiple sticky notes, butcher paper taped all over the room, several sharpies, and a few diet cokes I am happy to say that we began to create a plan to at least begin the journey.

It is always fascinating to me to learn from such amazing physicians and then translate their knowledge into the language of someone, or in this case, something else so that it can be understood.

The challenge I am solving for is time and attention.

My commitment is to find a way to provide more time to a radiologist to allow her the ability to spend more time focusing on their “why” of medicine…impacting the lives of patients.

It is to provide radiologists more time to spend with patients directly, having more time to educate their medical community on how to correctly order the right imaging exam to answer the right question for their patients, and to have more time to show how radiology (along with pathology) are not about cost centers, but are about how to deliver care at the N of 1.

If I can give radiologists time, they can then earn the trust they need to demonstrate their value in the new healthcare paradigm shift.

Radiologists earn trust at the N of 1.

Care is delivered at the N of 1.

As always you can feel free to email me at cancergeek@gmail.com or follow me on Twitter @cancergeek You can read past issues of my weekly newsletter by clicking this link: newsletter and feel free to signup using the subscribe tab at the top.

~Cancergeek

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