Implementing Artificial Intelligence in Health Apps for Better Tomorrow

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Implementing Artificial Intelligence in Health Apps for Better Tomorrow

Artificial intelligence and machine learning are two vital tools for insights. Without an AI engine, the data from a wearable lacks value.

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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 IoT can work without an AI engine. Why do we need it then? Because the true value lies in insights.

wearable healthcare app

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 engines 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 medical sources such as manuals, journals, and public health data to emulate a doctor’s knowledge.

Upon adding patient-specific data including 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.

Preventive Health

Google wants to inject nanobots into your arteries. But don’t be scared yet. If they could find a way to take them out, Google X could be the next breakthrough in med-tech.

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 or her wrist to receive readings from nanoparticles (the 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.

Medical Consultations

Upon detection of an abnormality, the patient can report them to their consulting physician or an AI doctor. An AI doctor is generally a stand-alone neural network with deep learning algorithms that can detect ailments faster than a human doctor can.

Deep learning algorithms ensure the platform makes minimal mistakes and makes a high amount of 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.

Medication Management

The AI doctor 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 from which he or she can order the medication 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 your medicine.

Ethical Grounds, Protocols, and Acceptance

In some cases, machine learning systems need to work 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 a house cat may appear similar to a computer.

In those cases, a crowdsourcing tactic like reCAPTCHA aims to improve the model further through human efforts.

One challenge is data integration and gathering data across dissimilar data sets. The connection between various schemas must be unstated before the data in all those tables can be joined.

Moreover, AI mobile app developers are increasingly using both SQL and NoSQL, structured or unstructured relational databases, and formats for data storage in accordance with AI-friendly wearable application development protocols.

ai ,ai applications ,data mining ,deep learning ,machine learning ,wearables

Published at DZone with permission of Noman Shaikh , DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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