For practical wearables and Internet of Things (IoT) implementations, Artificial Intelligence studies specific problem solving or reasoning tasks. Healthcare mobility solutions boast capabilities such as visual perception, speech recognition, and decision-making.
But wearables and the Internet of Things (IoT) work without an AI engine, why do we need it in the first place. Because the true value lies in insights.
Artificial intelligence (AI) and machine learning are two vital tools for insights. Without an AI engine, the data from a wearable would lack any value to the vendor as well as the user.
That’s the reason why, wearable app developers are increasingly adding AI Engine inside wearable health apps and wearable health solutions.
Moreover, AI assisted data mining is also essential to the success of an intelligent healthcare platform that ties many smartphones, website, IoT devices and wearables together to gather data and return intriguing health insights of an individual.
Building the platform-machine learning
The platform should contain data points from various medico-sources such as manuals, journals, and public health data to emulate a doctor’s knowledge.
Upon adding patient-specific data, effects of time and location to the platform’s enormous data set, the machine learning system can generate a clinical model of a patient.
Compatible medical wearables and IoT devices can interface with the platform’s API and can be made to exert interesting insights about the data received from the devices.
1. Wearables for preventive health
Google wants to inject nanobots in your arteries. Don’t be scared already. If they could find a way to take them out, Google X could be the next breakthrough in medtech.
Once injected via capsules, nanoparticles proactively detect and diagnose diseases, cancers, impending heart attacks or strokes based on changes to the person’s biochemistry, at the molecular and cellular level.
The patient then can use a wearable like a wristwatch clamped on his wrist to receive reading from nanoparticles (nanoparticles are actually IoT devices).
The wearable then feeds the data to the AI engine of the platform and utilizes its machine learning capabilities to detect abnormalities if any in the wearer’s body.
If detected, the wearable reports a potential condition like blocked arteries that could lead to heart stroke or cancerous tumor at a very early stage.
2. Wearables for medical consultation
On detection of an abnormality, the patient can report them to their consulting physician or an AI doctor. An AI doctor is generally a standalone neural network with deep learning algorithm that can detect ailments faster than an actual doctor can.
Deep learning algorithm ensures the platform makes minimal mistakes and maximum detections through a self-learning module.
While it shares the same data as the platform, the machine learning algorithms are stronger in nature, delivering detailed reports.
3. Wearables for medication management
The AI doctor based may prescribe you medication. Under the surface, the neural network that powers the AI doctors upon detection connects to the platform to gather required medical data and prescribe medications to the patient.
The prescription is then sent to the patient’s wearable which he can refer to or even order the medication over using the integrated contact-less payment system with the NFC chip embedded in the wearable.
A wearable health app can even remind you when it is time to take a medicine.
Ethical grounds, protocols, and acceptance
In some cases, machine learning systems need to be amalgamate with software codes to produce improved results.
Depending on the subfield, some structures can’t attain a high degree of accurateness without human intervention, such as in the instance of identifying images. A wild cat and house cat may appear similar to a computer.
In those cases, a crowdsourcing tactic like reCAPCHA aids improve the model further through human efforts.
A challenge is data integration, gathering across dissimilar data sets. The connection between the various schemas must be unstated before the data in all those tables can be joined.
Moreover, AI mobile app developers increasingly using both SQL and NoSQL, structured or unstructured relational database, formats for data storage in accordance to the AI-friendly wearable application development protocols.