7 Key predictions for how AI will Impact the Pharma industry

AI in pharma is coming. There will be a few bumps in the road in terms of getting it in place but once it’s ready, it will revolutionize the healthcare industry.

In fact, 70% of businesses believe artificial intelligence will play a very important role in the industry. Furthermore, that 62% of businesses are thinking of investing in AI very soon.

So, with all this in mind, here are 7 key predictions for how AI will impact pharma.

1. Increase Speed Of Drug Discovery

Pharma struggles with discovering new drugs. 9 in 10 clinical drugs will fail to make it to the trial stage. This means that a lot of time and money is wasted on developing drugs that will never see the light of day.

In turn, this keeps the price of existing drugs high, leaving those that need them the most unable to afford them. This applies especially to those that do not have the correct insurance if any at all, and those that can’t get the cover they need for the medication.

However, artificial intelligence will be able to increase the time t takes to discover new drugs, improve the success rate of drugs going to market through more efficient screening and testing processes and, ultimately for patients, bring the costs of medical care down.

2. Improve Drug Repurposing

While the pharma industry struggles to discover new drugs, it also has a problem with repurposing them. Too often are drugs thrown away after not being able to improve treatments they were intended for.

However, AI in pharma will increase drug repurposing because the technology will be able to check what the drug is made up of and test if it’ll work on other diseases and conditions. The AI will also be able to identify the population that will benefit from the repurposing.

This will have great effects on bringing the cost of healthcare down for both the patients and pharma companies. Patients can get access to more drugs while pharma companies save money by not needlessly throwing the new drug away.

3. Find Reliable Clinical Trial Patients Faster

Clinical trials provide medical professionals with very useful information about new drug performance and to create better treatments than what’s currently available. The tricky part is finding the best people to take part in the correct trials.

This is where AI will come in. Artificial Intelligence in Pharma is predicted to significantly reduce the time it takes (and cost) of finding the right patients, enabling professionals to study the effects of new treatments quicker.

Because AI is automated, as soon as new data is entered, data can be processed and used to find the ideal candidate and enrol them into the study.

4. Provide Better Insights Into The Clinical Trial Data

Pharma companies also struggle with analyzing the data they receive from successful clinical trials. With all the information entered, staff are required to prioritize their time on the most important aspects of the trial to ensure they get the most out of the data.

This is where AI will be able to improve current systems. Through machine learning, the information from the clinical trials can be analyzed much quicker and more efficiently. It also removes human error from the analyzing stage.

Once the data has been analyzed, it can be passed to the right medical professionals to make better decisions as to how to proceed with the drug or treatment.

5. Better Drug Design

It is also predicted that AI will impact the pharma industry by design drugs using computational methods:

“Cognitive computing entails the imitation of human thought processes through pattern recognition, natural language processing and machine learning by self-learning systems with the aim to build automated computerized models with the capacity to solve problems even without assistance from humans.”

This will enable pharma companies to begin the drug discovery and development processes from the very beginning, through using computational and predictive algorithms to construct molecules.

The benefit of this impact will be improved efficiency of drug design, Companies will be able to create brand new drugs, reduce their side effects and remove certain structures from existing drug molecules that are known to cause problems or don’t prove to be that effective.

6. Increase Rare Disease Diagnoses

Rare diseases are tough to diagnose for the simple fact that they are rare. One of the ways to combat that is by having funds in place to ensure that equipment/facilities/systems are in place to help diagnose these conditions.

However, pharma has limited funding, meaning that the industry faces difficult decisions as to where the money should be allocated. This can be particularly problematic for patients that suffer from rare diseases because they can often go overlooked.

It is predicted that AI can help these people. As we have seen from previous points, AI will make the drug discovery process cheaper and more accessible, including rare diseases.

Pharma companies will be able to leverage AI to explore common symptoms and causes of rare diseases with known conditions in order to improve diagnosis and find potential treatment options.

7. Enable Real-Time Results

Finally, it is predicted that AI will revolutionize the healthcare industry by providing practitioners will real-time, on-the-go results for their patients.

This is already happening thanks to the introduction of smart devices and wearable technology such as heart monitors and healthcare apps.

As more data becomes available, the more healthcare can be personalized to the individual. Patterns will emerge and the AI will be able to analyze far quicker and more efficiently compared to a human.

Not only will this improve the speed of results, but it will also strengthen the relationship between the pharma industry and the patients.

Conclusion

These are 7 key predictions for how AI will impact the pharma industry. Some applications are already in use while others still have a bit to go until they become the norm.

Nevertheless, one thing is or sure: the AI era of healthcare is coming and the benefits it will bring could not come soon enough.

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