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I think the title is fine, but a lot of the comments are applying what the software does to medicine in general. What this software is used for is very specifi
by Herodotus38 9y ago
I think the title is fine, but a lot of the comments are applying what the software does to medicine in general.
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.
- gumby 9y ago> 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. This is one of the classic AI application domains -- for example this is why Stanford's Knowledge Systems Lab was near to the medical school and why the mainframe Zork was developed was owned by MIT's Medical Decision Making group.
- ska 9y agoPHI is a real barrier to your wish list, it's harder to get right than most people think and it is very very hard to get right in a distributed environment. And all the current advances in these general areas (voice recognition, transcription, semantic reasoning, etc.) are leaning heavily on distributed processing. It's definitely a well identified market and there are people working on it, but I haven't seen much progress (not that I'm looking terribly closely at the moment). From the point of the view of the last step, the ML one, I suspect the consenting is as much an issue as the PHI. Getting any real traction on this will likely require massive data sets and significant clinician time to aid training.
- Herodotus38 9y agoThe barrier is high enough that I heard that Microsoft is working on this with UW by using pretend patient encounters in an attempt to bypass PHI and get a database.
- nradov 9y agoEric Schmidt (former Google chairman) proposed building an AI scribe during his HIMSS 2018 conference keynote address. He claimed it should be possible within 10 years. I hope he's right, but I suspect he underestimates the difficulty of building reliable clinical systems. http://www.himssconference.org/education/sessions/keynote-speakers http://www.himssconference.org/education/sessions/keynote-sp...
- Herodotus38 9y agoThank you for the link, this is what I'm interested in.