How to Utilize Tesla’s 3 Favourite Tech as Healthcare Innovation Technologies in the USA?

The Tesla IoT case study is epic.

Tesla is using IoT technology in its core part, the motor that drives the car.

Tesla’s motor is IoT-enabled which allows the Tesla team to upgrade the range of the cars with a software update.

Elon Musk did the same during the time of Hurricane Dorian in Florida.

Tesla boosted its Model 3 car range to help people travel further away from the hurricane.

Another example of Tesla using IoT is its mobile app.

Using it, Tesla car owners can control the entire car remotely.

In case you’re wondering, here is its basic working methodology,

• The user using the Tesla app gives a command
• The app sends that command to Tesla’s central ECU which has a separate IoT controller
• That IoT controller processes the command and either turns on the car or sets the climate, as per the command

Utilizing IoT in Healthcare as a Healthcare Innovation Solution

For enhanced outcomes, doctors and care teams must monitor patients’ health continuously and make recommendations accordingly.

However, it is not feasible and practical.

But with IoT technology, remote patient monitoring is now possible which unleashes the potential to keep patients safe while keeping a round-the-clock eye on them from anywhere.

Remote patient monitoring can be achieved with the fusion of IoT-enabled medical devices and software or mobile apps.

The IoT-enabled medical devices collect the clinical reading and the app shows it to users.

The best part of remote patient monitoring is that it alerts the care team if a vital body sign falls below a certain limit or crosses a certain threshold.

Such an advanced alert saves patients’ lives and reduces their hospital stay.

Other potential areas where you can utilize IoT as a healthcare innovation solution are,

• Emergency care
• Tracking of inventory, staff, and patients
• Augmenting surgeries
• Virtual monitoring of critical hardware
• Pharmacy management
• Fall prevention and detection
• Ambulance fleet management
• Medical waste management
• Medical supply chain management
• Hospital at home

IoT in Healthcare: A Case Study

Sensing the future market scope, a telemedicine app owner approached us to add a remote monitoring module to his telemedicine app.

He had a simple requirement, the medical hardware and software (his telemedicine app) must communicate seamlessly.

But that simple requirement was challenging as compatibility issues are very common in IoT-enabled mobile apps.

However, with our healthcare-specific skill set and with the help of IoT integration standards, we managed to achieve it with zero compatibility issues.

The outcome was remote monitoring of a patient’s vital body signs (blood pressure, temperature, oxygen level) directly through physicians’ telemedicine app, resulting in real-time data-driven virtual care.

AI is in the heart of Tesla.

Without AI, Tesla would never be what it is today!

Using AI technology, Tesla is making its ‘machine’ think like a human and make decisions by itself.

Tesla’s custom driver-assistance program known as autopilot leverages cameras and sensors to collect real-time data and leverages its AI capabilities to process it and make decisions such as

• Switching lanes
• Applying brakes
• Maintaining a safe distance from other cars

The more data Tesla’s autopilot gets, the smarter it becomes!

The most epic way Tesla is using AI is by making every other car aware of what a single car’s AI system learns.

For example, if car A struggles to make a right turn at a particular intersection, it learns from it and makes other cars aware of what to do at that particular intersection.

This way, Tesla’s AI system works on both historical data and real-time data to make decisions without human intervention!

Utilizing AI in healthcare as the healthcare innovation technology!

Precision is what matters the most in healthcare.

But healthcare professionals have to pay a cost in the form of their time to be most precise every time.

Sometimes, it is not at all possible to be most precise even after investing more time in care delivery.

Meaning, there is a desperate need for someone or something that assists providers in decision making and delivering precise care.

Here, the most reliable and accurate solution is AI.

The way Tesla’s AI analyzes real-time data compares it with historical data, and makes decisions by itself.

The healthcare AI can also analyze real-time clinical data, compare it with historical data, and come to the conclusion that works as supportive data for clinicians in clinical decision-making and diagnosis.

Yes, AI cannot ever replace clinicians.

But it can certainly help clinicians in decision-making and diagnosis.

Some other use cases of AI in healthcare are,

• Patient prescribing
• Early detection of disease
• Symptom checker
• Drug discovery
• Personalized care
• Medical imaging insights
• Administrative tasks management
• Assistance to emergency medical staff
• Virtual nursing assistance/ chatbot

AI in Healthcare: A Case Study

A doctor wanted to build a chatbot-type symptom checker to help people identify what causes their common symptoms without even consulting a doctor.

Since there are thousands of symptoms and hundreds of diseases, we could not manually set rules for each symptom and disease associated with it.

So, we utilized AI techniques and a machine learning algorithm called a genetic algorithm.

The genetic algorithm helped us to create rules based on a large data set of symptoms and illnesses.

The rules were in the form,

If X is the symptom, Y would be the cause.

The rule engine of the app prepared 10,000+ rules with the help of historical data we trained the algorithm on.

Additionally, it also keeps making rules with the data users are providing.

So, now when any user adds his symptoms, the symptom checker’s AI modules compare that data with the rules and come to a precise conclusion because it now has many thousand rules based on real data.

AI has a limitation.

It can very easily process and understand data which is in the form of text and numbers.

But when it comes to understanding visual data such as images and videos, it natively lacks capabilities.

Computer vision is giving AI, the capability to understand images and videos & act accordingly.

Tesla calls it Tesla Vision which works with the ultimate collaboration of cameras.

Whatever Tesla’s 8 cameras detect including traffic lights, overhead lights, road markings, moving objects & road signs, Tesla Vision processes it, understands it, and acts accordingly.

Utilizing Computer Vision in Healthcare as a Healthcare Innovation Technology

Understanding medical images is the most crucial task for doctors to diagnose patients accurately.

However, many times doctors end up missing important details in medical images as it is not feasible for doctors to identify the micro details.

With computer vision, doctors don’t have to spend time analyzing medical images.

The computer vision tech itself analyzes the medical images, notes down major observations, and sends reports to doctors.

The best thing about computer vision is that it can track the overall healing progress of any wound or fracture and give a progress report to the doctor.

Other areas where computer vision as a healthcare innovation technology can be implemented are,

• Tumor detecting
• Cancer detection
• Medical training
• Body movement monitoring
• Inspect X-rays, CT scans, and MRIs
• Detecting skin cancer
• Detect anomalies
• Egg predication
• Bone image understanding

Computer Vision in Healthcare: A Case Study

A dermatologist wanted to achieve the impossible by letting patients know whether they have skin cancer or not by simply uploading an image of their skin.

The challenge we faced was to train the model not only on AI and ML but on deep learning too.

Because computer vision is the segment of deep learning.

We trained the model with many thousands of skin cancer images which we collected from different legal sources.

The data discovery was itself so complex that we utilized another AI module here.

Due to the large number of skin cancer images we trained the model on, the model is now even capable of identifying the skin cancer type with just the image of the skin!