A couple of weeks ago, Qualcomm announced they are sponsoring an X-prize to come up with a health care device. The prize is $10 M and the device must diagnose 15 diseases — it doesn’t say which ones. The goal is to make a consumer device, dubbed a tricorder, that people can use in their homes to provide medical care, which requires advances in sensor technology, medical diagnostics and artificial intelligence, among other things, according to the site.
In reading the overview, there is a section about the potential trade-offs a design team in the competition will have to make. One of those mentioned is the placement of the AI engine. I think a more important concern is what kind of artificial intelligence would be in the device and how it would interact with the sensors and the user. From that the best placement of the AI engine would probably become pretty obvious.
The authors of the overview are right to draw attention to the AI in the tricorder, as diagnoses, seems to be the true intent of the device. The emphasis is not so much on the accuracy and resolution of the sensors themselves that are used — their resolution will be determined by what is just good enough to make an accurate diagnosis. Instead the real focus of the competition is in the diagnoses, the intelligence, and this is ripe for machine learning.
One of the most important machine learning functions in the device will be its natural language processing. Telling the doctor your symptoms is still a major aspect of any patients experience with any medical conditions. We’re not yet at the stage where people can just submit to series of measurements and get a diagnosis. From both standpoints of the patients comfort level with the device as well as our own understanding of medicine, any effective diagnosis machine will have to be able to understand a person’s description of why they are consulting the device for medical help in the first place. A major aspect of any medical diagnosis is how the patient feels, how much pain they are experiencing and how the condition for which they are seeking help is affecting them. Without a sensor that can accurately measure pain, we have to rely on the patients words.
The effective tricorder will have to navigate the language as well as incorporate information from sensors to arrive at an effective diagnosis. I think this is the first consideration when designing the tricorder: how will it parse the patient’s description of the problem. Then blend in the information from the sensors to complete the story.