FDA permits marketing of AI-based device to detect diabetes-related eye problems

Some of the most elegant code is terse, but it takes years of education, experience, and intelligence to be able to produce that logic.

http://three-men-excerpt.pen.io/

What this software is used for is very specific, but also very useful in that it is a common medical problem. It is used only to help diagnose diabetic retinopathy (ie eye damage caused by diabetes).

This is AI Vision software used to analyze a photograph of someone's retina to detect damage. In essence it is much more like the programs that are used to analyze chest X rays to detect pneumonia that have been recently published. Where this is useful is that it can probably cut out a lot of human work in diagnosing retinopathy, however it is an incremental step. Even when I was a resident in a primary care clinic years ago the process was somewhat automated like this, with our medical assistants taking a photograph with a special machine, and then this photograph would be digitally sent to a specialist (I presume an opthalmologist but I could be wrong, maybe optometrists can be licensed to do this) for interpretation.

What this isn't, is diagnosing a patient based on taking a history and inputting examination findings and labs, etc... We are still quite a bit of a way from that but I'm sure people are working hard on that as well.

EDIT: In my opinion, where AI could really make a huge difference for my work as a hospitalist (a doctor that admits and rounds on hospital patients) is in voice recognition software, with eventual language processing to help me write notes faster. First, give me a program like dragon dictate but which I can use in the patient's room (obviously one would have to figure out the HIPAA compliance issues) that transcribes my voice and the patient or family member into a readable text file that I can review when I write their note.

Next step would be that same program can give me its attempt to summarize our interview into a reasonable note, which I can edit for accuracy. This would be in effect an AI scribe. A scribe, for those who don't know the medical jargon, is a hired person whose only job is to listen to a doctor interview a patient and help write medical notes, they are usually young pre-medical students. It's a relatively new position that became created as the burden of documenting in electronic medical records limited the amount of time providers could spend with patients. Very common in Emergency Medicine where high output is needed, sometimes also in primary care.

Next step, is you have a company with all this protected medical transcription data and eventual medical outcomes, and you use ML to find algorithms to try and tease out what variables ended up being the most useful for accurate diagnosis. Before that you could have the program prompt the doctor for questions that it thinks would be helpful, etc... Again, huge medico-legal barriers to this but there is a roadmap to becoming a billionaire in my opinion.

The way it works is: the manufacturer of a medical device assesses the harm that can be caused by a software malfunction, and assigns it a safety classification (class A, B or C). Class A is used when no injury is possible, and class C is used when death or serious injury is possible (e.g. a surgical robot). The manufacturer also provides a "failure modes and effect analysis" document that looks at everything that could go wrong, what is the likelihood of the failure happening and what is the effect on the patient.

Based on the safety classification, IEC 62304 requires different levels of rigour. For example, the standard only requires blackbox testing for class B software, whereas for class C software it requires whitebox tests as well.

The manufacturer also needs to come up with a software development plan that ensures that all of the requirements of the standard are met, and an "argument" (supported by test reports, process documentation, source control history, etc) that the software was developed according to the plan.

And that is what the FDA audits: they look at the development process of a given feature and they check that the plan was followed. I think they rarely delve into the details of the implementation and are generally just checking that the safety arguments are sound and supported by evidence.