Challenges for Artificial Intelligence in Medicine

The most pressing thing to understand is that clinicians spend the VAST majority of their time gathering all of the necessary information to make a diagnosis. In other words, they aren't puzzling over how to diagnose about 85% (made that up) of their patients.

Once the necessary information is gathered, an experienced doc doesn't usually spend more than about 10-15 seconds debating different diagnoses. Therefore, if your tool takes more than 10-15 seconds to launch, enter any necessary data, and get a result, you are slowing the clinician down and they won't use it. This is why automated EKG interpretations (which are very much a real thing used at hospitals across the country) print directly on the EKG printout - it doesn't cost the clinician more than about 2 seconds to read what the machine thinks and adjust their interpretation accordingly[1].

One of the major problems limiting adoption of "expert" computer systems is the amount of (very expensive) integration it takes to get them under that 10-15 second limit. One of the big reasons radiology is seeing a lot of buzz around machine learning and automated interpretation is that integration becomes a lot easier when you can just feed in an image and maybe 5 words about the indication for the study.

I would love to go on for a while about this stuff, but I'll stop there for now :)

[1] Some people here might be interested to learn that non-cardiologists generally don't have negative views about automated EKG interpretations. But we are also very well-aware that when we make decisions about a patient, those decisions have to be anchored to something a lot more substantial than "the machine told me to do it."

Take ECGs -- it's true that in a hospital, an automated ECG interpretation doesn't buy you much. But what about about the patient with a paroxysmal heart rhythm that doesn't show up when they're at the doctor's office?

I was at a patient conference recently, and people were describing the first time they felt atrial fibrillation (a common abnormal heart rhythm). Many times, by the time they got to the doctor, they were back in sinus rhythm and thus the ECG showed no abnormality. Some were told they were just feeling "anxious" or "going through menopause." It often took months of persistence just to get a diagnosis.

Now, if have cheap sensors + AI analyzing the patient's whole heart history before they walk in the door, you can do a lot of good for real people.

Building a "database" of information from which to make a diagnosis is unlikely to be easily automated. Take a straightforward case of a patient who comes to the emergency department after "fainting". Did they slowly kind of "melt" to the ground, or did they just BOOM fall? Were they confused after they woke up, or just a little sleepy? Was it a hot day or is it wintertime? Were they wearing a shirt and tie, or a t-shirt? Different answers to each of these questions will change the probability of each potential diagnosis. The signal:noise ratio is frequently very low, and there's not a great way to improve it without adding an extremely large amount of cost and time to an already expensive and slow healthcare system.

Good clinicians already have an idea of the top 2-3 most likely possibilities before they walk into a patient's room, based on epidemiology and a quick review of a patient's chart, but we try to be flexible enough to discard those preconceptions if new info becomes available. Sometimes clinicians fail to fully investigate what a patient is telling them, and that's where the real mistakes get made.