There’s a stone in your gall bladder and two in your left kidney

I was just reading an interesting article on the World Economic Forum website which speaks about seven interesting ways in which AI is being used today in healthcare.

Imaging seems to be a core application area and it’s not just about radiology.  Skin cancer diagnosis for example. That’s an imaging and recognition problem.

Eye scans is another imaging application. It appears that the two areas where AI is already doing very well is in the diagnosis of diabetic retinopathy and age-related macular degeneration (AMD).

I found the reference to brain scans and the corresponding imaging application very intriguing. The AI looks at the brain scan and is able to make a better prediction than a human doctor on the probability that a person in a coma will recover.

The radiology and cardiology applications. As also the interpretation of CT scans are more straightforward and intuitive. Here is a link to the article –

I am looking forward to big leaps in diagnosis via imaging at a microscopic level and spectrometry. How great it would be to diagnose common infections or micro nutrient deficiencies or most of the blood work that a lab does today; by simply shining a light on a drop of blood or maybe through the skin or pointing a camera into the eye. The machine interprets the image and gives you an almost instant diagnosis. Not just of an existing disease but also the likelihood of getting one?

The nature of imaging and interpretation is changing too. Soon we will not have the need to get into big machines for images from inside our bodies. Imaging could be a continuous affair using devices we can wear. This is already true for ECG. If the work of people like Mary Lou Jepsen becomes real, that’s what we will have. And surprisingly she uses sound to look into into the body. There’s an article about her work here – link –

This will mean that diagnosis is not a matter of looking at an image and comparing it with a knowledge base of images. It will be looking at a series of images. Hundreds or thousands or millions of them to identify patterns that point at something amiss. This is the amount of data that a human being will never be able to handle. An explosion in data will necessarily require machines to interpret the data. That’s the future of imaging and AI in medicine.