Tiny AI: Introduction, Approach and Application in Healthcare Industry
May 19, 2021
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Over the years, we have witnessed AI (Artificial Intelligence) transforming industries around the world and the healthcare segment has not remained untouched. And with evolutionary concepts such as Tiny AI, the healthcare industry will undergo digital transformation through health tech.
AI-assisted robotic surgery, virtual nursing assistants, virtual diagnosis, administrative tasks in hospitals, etc. are some of the AI-enabled practices largely adopted by healthcare professionals. In addition, there are notable breakthroughs such as data analyzing through AI-based tools; visits to the clinic, medications, lab tests, procedures performed, track of symptoms, etc., that are transforming patient care, diagnosis and even preventing chronic diseases.
Now that we have established that technologies such as AI will pave the way to the revolutionized healthcare, let’s understand the complete scenario of hurdles and solutions.
So, what’s the challenge?
Medical science is improving like never before, and therefore, the rising life expectancy (longevity) comes with an increase in demand for medical services, resulting in rising costs, workforce and much more.
By 2050, one in four people in Europe and North America will be over the age of 65—this means the health systems will have to deal with more patients with complex needs. (Source)
AI-based solutions have become an integral part of our lives, however, they have their challenges such as large sizes of data, algorithms, etc., and environmental footprint. For any technology to help us move forward, it must be sustainable.
According to researchers at the University of Massachusetts Amherst, a single AI can emit as much as 284 tonnes of carbon dioxide which is equivalent to five times the lifetime carbon emission of an average car. (Source) When it is not only hazardous for the environment, it is also obstructing the speed and privacy of AI applications.
With the initiative in Tiny AI that is focused on reducing the size drastically along with reducing the environmental footprint, it has the potential to eliminate many problems in one go.
What is Tiny AI?
Tiny AI is a concept/term used to describe the efforts of AI practitioners to reduce the size of algorithms, especially those that require large amounts of datasets and computational power.
Tiny AI is based on distillation methods that
Reduce the size of a model
Maintain high levels of accuracy
The conventional model can be significantly reduced to 10x, and a smaller algorithm can be created to make decisions on the device, rather than on the cloud.
What is the Role of Tiny AI in Healthcare?
With Tiny AI-enabled wearable medical devices and strategically gathered medical-grade data, health professionals can consistently monitor the health conditions of their patients, which can be useful in providing personalized medicine and treatment.
The Tiny AI-enabled medical devices can provide data to gather actionable insights.
Integrated Approach in Tiny AI
In order to utilize the power of Artificial intelligence, one needs an integrated approach that tactfully integrates innovations in data usage, hardware as well as software with the same efficiency.
The shift from cloud dominance to decentralized is evident and is being largely accepted, which only strengthens the part of Tiny AI. Edge and extreme edge devices are/will be developed in a way that they can do their own processing. While sending the minimum data- the only data that is required, they will be able to work and learn together.
When cloud centres will support high-performing computing applications, other decisions based on data can be taken in a few hours, instead of days.
For smart data usage, smart data gathering and management are required.
Data reduction with surrogate modeling
Alternative data sources, for example, radar sensing systems
Unsupervised learning methods
AI-enabled data processing
As Tiny AI is an approach that follows, accomplish more with less, reducing the hardware is the key.
A new architecture based on nano approach
New structure such as 3D integrated systems
Advanced and new materials and solutions
Shifting to Tiny AI means shifting to on-chip AI algorithms that fulfill the smart requirements.
Edge-learning methods like joint learning
ANN architectures such as spiking
Sensor fusion strategies
Adaptive inference techniques
Transfer learning approaches
What are the Benefits of Tiny AI?
Incorporate Energy Efficiency
When a large transmission of datasets in AI consumes a lot of energy, Tiny AI follows the decentralized approach, saving time and energy while data processing.
Transmission of data may violate privacy whereas, Tiny data eliminates the transmission or reduces it to the extent where it is necessary. Since it remains on the primary devices, there is an enhanced level of privacy.
In a nutshell
The AI technology has demonstrated its potential to revolutionize healthcare by scaling health tech that aligns with the rapidly improving medical science. When AI simplifies the lives of patients, doctors and hospitals, Tiny AI enables them to make that process more efficient, less time-consuming, and all the more reliable.