The ultimate goal of artificial intelligence is to train algorithms and computer systems to think, behave, and learn as humans do. If an algorithm can become completely self-learning, then it can carry out human tasks, train itself to get better over time, and perhaps, train other algorithms to do the same. The ability to think, learn, and self-evolve are major hallmarks of human intelligence and if they can be replicated in machines, the future possibilities are endless.
Combining the efficiency of a machine with the intelligence of a human being will have a groundbreaking effect on all major sectors of the world.
The promise of such an effect is what has driven the pharmaceutical industry to spend billions of dollars on developing AI-powered solutions.
All of the top 10 “Big Pharma” companies are currently in the process of using AI to solve problems ranging from optimizing the discovery and development of new drugs to creating cures for rare diseases.
The question now is: why should Big Pharma invest further in AI in 2019? Has the industry seen enough progress from artificial intelligence to justify a further outlay on developing the sector? Can AI really solve the problems that Big Pharma is grappling with?
The industry has seen marked progress over the past 5 years
For example, Novartis is using machine learning algorithms to find untested compounds that show promise in discovering new treatments. They use the algorithms to run comparisons between diseased cells that have been treated with various compounds; the aim is to find out which compounds performed well enough to warrant further research.
Not long ago, Bayer and Merck received recognition from the FDA for creating AI software programs that make it easier to diagnose chronic thromboembolic pulmonary hypertension (CPTH). The condition has symptoms that are similar to those exhibited by COPD and asthma, and doctors have been struggling to diagnose it accurately. Globally, five million people suffer from this condition every year.
According to this article by Digital Authority, Verge Genomics, Cyclica, Bayer, AstraZeneca, and many other big pharmaceutical companies are also building real-world solutions using artificial intelligence. There is no reason for them to stop in 2019.
The prevalent issues facing big pharma remain unsolved
Despite the progress that has been made so far, some of the biggest problems that pharmaceutical companies face remain unsolved. Since the use of artificial intelligence has been yielding tangible results, there is no justification for Big Pharma to abandon its investment in the sector in 2019; especially since there is no viable alternative.
For example, Big Pharma’s AI drive is focused mostly by the impetus to accelerate the drug discovery timeline and optimize the process; this should not come as a surprise. At the moment, it costs too much to develop a drug and bring it to market. Furthermore, many drugs fail to become commercially viable and pharmaceutical companies have to up the prices of the ones that do, to remain profitable.
Healthcare Weekly reports that it cost about $1.2 billion to develop a new drug, and 9 out of 10 new drugs fail to make it to trial.
Other prevalent problems include finding cures for diseases that have none (e.g. Parkinson’s, ALS, Alzheimer’s etc.); managing drug research more efficiently and ensuring adherence of trial subjects, and improving the quality of treatment available to patients that suffer from rare diseases.
Artificial intelligence’s quest to provide viable solutions
So far, artificial intelligence has proffered viable solutions to tackle major problems. While a number of the solutions require more work and fine tuning to be adopted across the board, the potential is there.
Cyclica and Bayer are working on artificial intelligence that will make it easier, faster, and cheaper to discover and approve new drugs. Novartis is working on something similar; as are Boehringer, Verge Genomics, Merck, and many others.
Furthermore, Abbvie is working on an AI solution to improve drug adherence; Bayer is using AI to improve pharmacovigilance during clinical trials; Bristol-Myers Squibb, Pfizer, Mayo Clinic, Medtronic, and a host of other drug companies are working with IBM Watson to improve clinical trial matching.
All over the industry, Al-powered solutions are springing up to tackle the major problems. The expectation is that in 2019, further progress will be made; this is not the time for Big Pharma to start rethinking their AI investment. On the contrary, this is the time to double down.
There are notable new entrants in the sector
Spurred by the progress of the market leaders, healthcare companies from all over the world are working on creating their own AI solutions.
For example, in the UK, the NHS is looking to leverage the technology towards solving some of its problems. New medical technology centers are to be opened in London, Oxford, Leeds, Glasgow, and Coventry in 2019 and NHS have set aside £50M to establish the centers. For a start, these centers will use AI algorithms to hasten the disease diagnosis process.
Disruptive technologies are here to stay
According to Orthogonal, the growth of one disruptive healthcare technologies always compliments that of others.
The world saw a notable increase in the use of PCs in the 1980s; by the 1990s, the internet came around. About a decade after, we got social media. The growth of Myspace led to the creation of Facebook and Twitter; today, there are hundreds (if not thousands) of social media platforms.
Today, the world is seeing remarkable progress in the development of disruptive technologies like the increasing role Blockchain is playing in healthcare, the Internet of Things, Robotics, Big Data, Quantum Computing etc.
The more these technologies grow, the better for artificial intelligence. For example, the more “Big Data” in healthcare develops, the better the quality of data being fed to AI algorithms. The belief is that this will improve the rate at which algorithms learn and mirror human behavior.
Big Pharma does not need AI to reach full maturity before investing in it
There are 3 general classes of artificial intelligence: Weak AI, Strong AI, and Superintelligence. Weak AI lacks human intelligence and can only focus on specific tasks; strong AI has the ability to apply human-like intelligence to any task it is given, and superintelligence is AI that is smarter than even the brightest human beings.
Now, for Big Pharma to harness artificial intelligence effectively, it does not need superintelligence.
As a matter of fact, Big Pharma does not really even need strong AI to see a positive return on investment from artificial intelligence.
What it needs is weak AI that is not-so-weak e.g. an algorithm that retains just enough “experience” to recognize patterns, make decisions based on past experiences, and execute actions in varying contexts and situations. This level of AI is just above self-driving cars and given the success of Tesla in this regard, breakthrough can’t be far behind. And as long as these new algorithms pass FDA regulatory compliance rules, they are bound to produce a positive ROI.
Billions have been spent, progress has been made, there are no other viable alternatives, new entrants are making moves, and similar technologies are seeing significant development. These are all the arguments that support further investment by Big Pharma into AI in 2019. There can’t be arguments against it to justify any other course of action.
This article was contributed by Julian Gnatenco @ JGBilling, one of top medical billing companies in Chicago
Article Submitted By Community Writer