Biotechnology has taken modern society
by storm from its crucial role in agriculture, medicine, genomics,
immunology and other scientific and nonscientific fields to its vital
contributions in the race to discover the covid-19 vaccine,
Biotechnology permeates almost every aspect of society.
The biotech industry, or the broad area
of biology involving the use of living systems to develop products using technology,
is growing at an unprecedented pace in modern society.
In fact, according to a new report, global biotech drug sales generated
290 billion dollars in 2020, and the market size is predicted to reach
2.44 trillion dollars by 2028.
This massive growth is thanks
to the explosion of big data in the last few decades
that allows biotech scientists throughout the world to gain valuable insights,
analyze new trends, and make informed predictions.
But as the amount of data, as well as
the dire need to utilize this data for the benefit of humanity
increases exponentially, it has become critical for scientists to successfully harness
this vast amount of structured and unstructured data to perform
drug manufacturing, sequencing of DNA and RNA, chemical analysis, etc.
at a rapid pace and without any manual errors.
So how can this indispensable industry
continue to keep up with the surge of big data and latest advancements?
Well, this is where Artificial Intelligence comes into play.
Revolutionary AI software and Machine Learning algorithms
play a transformative role in effectively harnessing
the plethora of data available in this industry and in enhancing productivity,
speed and power, for a diverse spectrum of operations.
The biotech industry has several subsectors,
namely, agricultural biotech, animal biotech, industrial biotech
and medical biotech, that skillfully leverage the advancements
of Artificial Intelligence and Machine Learning technology.
Let's take a quick look at how each of these sectors are enhanced through AI.
Agricultural biotechnology serves to develop genetically modified plants
to increase crop yields and introduce desired characteristics in them.
The subfield uses AI techniques
to program autonomous robots to harvest crops at a much faster rate than humans,
develop computer vision algorithms to process and analyze data captured by drones,
to monitor crop and soil health, and deploy machine learning software
to track and predict environmental changes that impact crop yield.
Animal biotechnology is a branch
that applies molecular bio techniques to alter animals genes to improve their
sustainability for industrial and agricultural purposes.
Selective breeding at a molecular level is where favorable genetic characteristics
among animals are selected, and these animals are bred.
Machine learning predicts the expression of these genes, thus helping scientists
to make informed decisions when performing artificial selection.
Industrial biotech involves the production
of new enzyme catalysts to maximize and optimize biochemical pathways that can
be used in manufacturing. Computer Aided Designs
and AI are helping generate accurate
molecule design and machine learning helps calculate permutations and combinations
of different chemicals to reveal an accurate formula without having
to conduct lengthy, manual experiments in the lab.
Not only does AI play a crucial role
in enhancing operations in labs, but it also plays a core part throughout
the life cycle of the drug to the point where it reaches the market
Operations that usually take 5 to 10 years, now only take a couple at most
with the use of Artificial Intelligence in Biotechnology.
Medical biotech produces drugs and antibiotics to better human health
and genetically manipulate cells to produce beneficial characteristics.
Machine learning and computer vision are commonly used in the detection of cancers
and other diseases as it makes the identification more precise
by improving upon itself every time the algorithm is run.
Basically, as the number of diagnoses
increases, the machine learns what is right and what is wrong on its own
and inherently becomes more intelligent at diagnosing diseases correctly.
AI algorithms, particularly Convolutional Neural Networks,
have propelled medical image analysis forward at a rapid pace
previously only done manually by physicians.
Moreover, AI machines have dramatically reduced diagnostic and therapeutic errors
by evaluating large sets of data against multi-dimensional and constantly changing
criteria based on patient preference and well-being.
And this personalized medicine is a valuable aspect of AI technology
in the medical field. Machine Learning is used to revamp
treatment plan based on the patient's personal wellbeing history,
which offers better health management and lowers costs overall.
Machine Learning and Deep Learning can also
reduce the radiation therapy planning process to just minutes,
saving radiologists days, and improving patient care.
And several biotech firms have also realized the immense value of AI
for their business to expand accessibility, increase efficient decision
making and predictive analysis, and decrease costs.
And many companies have invested
in utilizing AI software as the core operator of their new products.
It's estimated that there will be an additional revenue driven by AI
in biotech companies of 1.2 trillion dollars in the next three years.
AdamWise was the first company to apply a popular form of machine learning,
Convolutional Neural Networks, to drug design.
Convolutional Neural Networks can be seen in Alexa's speech recognition software
and Facebook's automated friend tagging feature.
Similarly, EyeNuk developed a product
called EyeArt that uses AI to detect disease from retinal images.
The detection of diabetic retinopathy was over 95 percent accurate in a clinical
trial, with about 950 patients spanning 15 medical centers across the U.S.
As another example,
a company called DesktopGenetics created a platform to design gene editing
constructs using CRISPER that works through AI. And DeepMind developed a system
called AlphaFold that utilizes machine learning to predict the 3D structure
of a protein given solely its genetic sequence.
And these predictions are rendered far more accurate than any other human
predictions or non-AI predictions from the past.
So it seems clear that Artificial
Intelligence technologies are revolutionizing the biotech industry
and these applications are becoming increasingly indispensable,
given that traditional manual methods are starting to reach their limits.
With AI technology significantly decreasing the manual labor and time required
to accomplish set tasks, health care providers and scientists can
better focus their efforts on other innovation driven goals.
AI technology plays an incredibly crucial
role in meeting the new demands of a constantly advancing society
and in expanding the potential of the biotech industry to propel forward
into new and transformative ventures for the betterment of humanity.
It's an exciting time ahead for the wonderful world of biotechnology
with the power of Artificial Intelligence. The future of Biotech is indeed bright!