In this era of apps, when apps have invaded almost every industry, the health sector is no exception. When you hear of wearables and Internet of Things (IoT), you may form an image of hardware, sensors, network, data, and some communication middleware.
However, you may think that healthcare wearables and IoT can work without an AI engine. Why do we need to integrate artificial intelligence (AI) then?
The real value of wearable health apps using AI and machine learning tools lies in the insights provided by them. AI is what adds value to the data from wearable devices by performing certain specific problem-solving and reasoning tasks that are useful for both for the user and the vendor. It is for this reason that AI engines are becoming indispensable tools for wearable health apps and wearable health solutions. Moreover, healthcare platforms that tie several smartphones, IoT devices, website, and wearables together depend highly on AI-assisted data mining for their success.
image source: gearpatrol.com
AI involves designing intelligent systems that can perform tasks requiring human intelligence such as speech recognition, visual perception, and decision-making. Machine learning, a subfield of AI, that learns and acclimates automatically through experience. Its focus is to make predictions based on the learned training data. Both machine learning and AI have infiltrated into nearly every aspect of our daily lives through various applications like Google Brain, Google Voice Search, Google translate, Apple’s Siri, Apple Watch, Xbox, IBM’s Watson, Netflix, credit card fraud detection, email spam filtering, autonomous car detection, and so on and so many others. Applying AI to wearables has many more practical applications, especially in various stages in healthcare. Thus, Android wearable app development company also needs to focus on integrating AI with wearable healthcare apps to make maximum benefit of this technology.
Machine learning builds the platform for wearable health apps
The database that will be used for building the machine learning platform for wearable health apps needs to take into consideration all data points from different medical sources including journals, manuals, public health data, patient reports, etc. to match a doctor’s knowledge. Clinical models of the patient can be generated by adding patient-specific data such as time and location to the vast data set of the platform. Data received from different medical wearables and IoT devices can be used to get interesting insights by interfacing with the platform’s API.
Medical diagnosis and treatment
Diagnosing the disease correctly and following the protocols for treatment is very essential. Various data points from textbooks, journal articles, and public data sets are used to prepare multi-dimensional probability distribution graphs as in Lumiata. These graphs can be used nurses to replicate and scale doctor’s knowledge for diagnosing and treating diseases. A clinical model of the patient can be generated by adding patient-specific data, effects of time and location to the massive data set in the machine learning system. Clinically approved wearables are expected to interface with the app’s API in the future for providing a constant feed of the physiological data, time, and place of the patient. This can be used for proactive monitoring and event triggers.
The importance of detecting diseases in early stages cannot be overemphasized. Research is ongoing on the use of nanoparticles to proactively detect and diagnose diseases such as blocked arteries, cancer, stroke, and impending heart attacks in early stages and prevent their harmful effects. Google X is using the theory that nanoparticles can be released into the bloodstream by swallowing a pill and while moving through the bloodstream they detect and diagnose diseases as movement of unattached nanoparticles in a magnetic field will vary from the movement of nanoparticles that are clumped around an abnormal cell, like, fatty plaques lining blood vessels that may break-free or cancer cells. This movement of these nanoparticles can then be used to detect any changes in biochemistry at molecular and cellular level. Using a wearable wristband or wristwatch the patient can view the readings from the nanoparticles. This reading is then fed to the AI engine of the platform and used to detect and diagnose any abnormality or disease in the person wearing it. However, the use of these injectable nanobots into the arteries is still being experimented and is expected to be the next breakthrough in medical technology.
image source: yourstory.com
Another nano-engineering and machine learning based early medical diagnosis platform that analyses the protein composition of biofluids using patterns and biomarkers to screen specific conditions is Entopsis. The biofluid sample to be analysed is incubated, and the Nanoscale Unbiased TExtured Capture (NUTeC) process is used to capture molecules. Machine learning algorithms for analysing the molecular signature on a NUTeC glass are applied to the sample, and the scanned signatures are compared with other signatures in the cloud database to find similar profiles and detect the condition in the early stage.
After a wearable health app detects an abnormality, the patient can report it to his/her consulting physician or an AI doctor. An AI doctor is based on deep learning algorithms of standalone neural networks and ensures that the platform makes minimal mistakes and detects diseases using a self-learning module.
Medication management involves prescription, purchase, and compliance with the medication. The neural network that powers the AI doctors may use the collected medical data when an abnormality is detected and use it to prescribe medications to the patient on the patient’s wearable. The patient can then order the medication from the wearable itself using the integrated contact-less payment system.
Medication adherence is without doubt very important as skipping doses or altering dose schedule while you get busy with something else can produce adverse effects or make the medication ineffective in totality. Though medication compliance is a complex, multi-layered issue, a wearable health app may be used to remind the patient when it is time to take the next dose.
An internet-connected pill-cap called the GlowCap has been developed to enhance medication compliance. It tries to improve medication adherence of the patient by blinking and making sounds the patient is supposed to take the medication. It also provides a combination of education, feedback, reminders, and incentives that alter patient behaviour and improve medication adherence. It also provides the facility of reordering by simply pushing a button on the cap. Caregivers also receive real-time data, such as when the medication is removed or a dose skipped.
Though machine learning and AI provide quick results, the results cannot be relied upon in all cases due to certain limitations. These limitations may be based on ethical grounds, protocols followed, or acceptance by people. One of these is the inability to achieve a high degree of accuracy without human intervention. For example, in identifying or differentiating certain images. Software codes of machine learning systems may need improvements for better results. Data gathering and integration of data across dissimilar datasets present another challenge while creating the database for machine learning.
With IoT, wearables, sensors, hardware, and communication protocols being used increasingly, you may be tempted to jump in for them, but remember that AI derived from data is the key differentiator of your solution. You can craft much more useful health apps using the powerful AI arsenal. Start applying the scale and power of AI today or hire an Android wearable app development company to build a higher valuation business and lead the competitive pack with us at IndianAppDevelopers.