I should read The New Yorker more often. I’m going to subscribe right now (done). The articles are fantastic. In the April 3, 2017 issue, Siddartha Mukherjee wrote a piece talking about automated medicine. It’s called A.I. Versus M.D.

Here is an excerpt:

His prognosis for the future of automated medicine is based on a simple principle: “Take any old classification problem where you have a lot of data, and it’s going to be solved by deep learning. There’s going to be thousands of applications of deep learning.” He wants to use learning algorithms to read X-rays, CT scans, and MRIs of every variety—and that’s just what he considers the near-term prospects. In the future, he said, “learning algorithms will make pathological diagnoses.” They might read Pap smears, listen to heart sounds, or predict relapses in psychiatric patients.

This is a world where you could snap a photo of your unsightly rash, submit it, and then have a machine provide a diagnosis. This is a world where early detection and prevention would become common place. Already, today, there are instances where learning-based algorithms (distinct from rule-based) have proven to be more accurate than raw humans.

I don’t know how you all feel about this, but I actually feel a sense of comfort thinking about this as the future of medicine. There’s piece of mind that comes with large amounts of data and the discipline of algorithms. At the same time, this is not necessarily about replacing humans. It is also about augmenting human ability.

If it leads to better health outcomes, then why wouldn’t we?

I should read The New Yorker more often. I’m going to subscribe right now (done). The articles are fantastic. In the April 3, 2017 issue, Siddartha Mukherjee wrote a piece talking about automated medicine. It’s called A.I. Versus M.D.

Here is an excerpt:

His prognosis for the future of automated medicine is based on a simple principle: “Take any old classification problem where you have a lot of data, and it’s going to be solved by deep learning. There’s going to be thousands of applications of deep learning.” He wants to use learning algorithms to read X-rays, CT scans, and MRIs of every variety—and that’s just what he considers the near-term prospects. In the future, he said, “learning algorithms will make pathological diagnoses.” They might read Pap smears, listen to heart sounds, or predict relapses in psychiatric patients.

This is a world where you could snap a photo of your unsightly rash, submit it, and then have a machine provide a diagnosis. This is a world where early detection and prevention would become common place. Already, today, there are instances where learning-based algorithms (distinct from rule-based) have proven to be more accurate than raw humans.

I don’t know how you all feel about this, but I actually feel a sense of comfort thinking about this as the future of medicine. There’s piece of mind that comes with large amounts of data and the discipline of algorithms. At the same time, this is not necessarily about replacing humans. It is also about augmenting human ability.

If it leads to better health outcomes, then why wouldn’t we?