The pharma and life sciences industry is faced with many hurdles within its way to success and growth, but there have been considerable improvements within the progress and successful development of projects. However, the increasing regulatory laws have been creating a ripple effect placing many blockages in the way of effective research and development within the life sciences and biotechnology fields.

These regulatory changes led by the far-reaching Patient Protection and Affordable Care Act (PPACA) in the US are leaving no choice for the pharma and life sciences industry to change their trajectory and that has been making it harder for the biotech and life sciences companies to bring out their best performances.

Life sciences industry has been playing a large role in the conservation of the environment and planet earth in general, but also producing the products that are beneficial for the mankind, taking the COVID vaccine for instance. Coming to the numbers, the global life science analytics market size is valued at 7.2 billion USD in 2019. As for the growth, it is projected to a compound annual growth rate of 7.9% from 2020 to 2027.

Here’s the market size in chart formation.

Among the factors that have contributed to the growth of life sciences, AI (Artificial Intelligence) has played an integral part in the exponential growth of the industry helping in weeding out the additional costs along with assisting the analyzation of large data sets in less time as compared to the many hours invested by human beings when performing it manually.

So, what exactly life sciences gain from incorporating AI-infused technology? Let’s have a look at it in detail.

AI in a nutshell; it enables data mining, engineering, and real-time algorithmic-driven decision –making solutions that enable the users to respond to the following key business areas:

• Easy Drug Discovery: Drug discovery has been made easy with the help of AI as the insights received with the help of cutting-edge technology has enabled the scientists to source authentic knowledge from external labs and other areas that reduces the timeframe of drug discovery and development of the product.
• Product Failure Prediction: One of the best things that have come out from AI technology is the fact that it can analyze the pre-existing data and predict any future anomaly regarding the product under development. The predictive algorithms of AI have proven to be of much use when it comes to predicting potential failures.
• Enhance and In-depth Reporting: To meet the changing regulatory compliance needs more effectively.
• Risk Management: For evaluation and other purposes that are posed by the elemental impurities in a formulated drug product.
• Real–time Medical Device Evaluation: Leveraging interconnecting data from implanted devices and personal care devices has made it fairly easy to gather mass data in real-time and made it possible for quick evaluation and analyzation.
• Intelligent Insights: Renew focus on understanding the underlying business data and generating insights and intelligence frameworks.

Keeping the business applications in mind, how else is AI contributing to Life Sciences?

The Few Ways Life Sciences in Using AI Today

Among the numerous ways AI is being used, here’s a few of the applications changing the Life Sciences landscape entirely.

1. Advancing Diagnostics

AI with its complex algorithms and pattern recognition along with automated measuring systems have enabled advanced and accurate diagnostics of otherwise difficult cases. With various kinds of data including images, textual, and other forms being received on a daily basis, swift diagnostics is unachievable manually. This has led to uncontrolled delays that have shown consequences. However, with AI technology onboard, the timeline to deduce, analyze, and conclude has been reduced considerably.

2. Advancing Research of New Products

Life sciences companies are constantly in a race to leverage AI in the newest and most optimal capacity in order to beat their competitors by identifying new possibilities for their existing and potential products.

Some of the examples include:

• Using advanced learning algorithms to mine data (structured and unstructured) to unveil insights that have the potential to identify new mechanisms and processes of disease and an improved design for any clinical experimentations.
• Fill the knowledge gaps through smart analysis of data sets available and refine the drugs on trials including the outcomes that the candidates have been showing to much more favorable ones.
• There is a plethora of knowledge and insights that can be discovered through real-time data extraction, break-down, and analysis that can aid further in research, scientific papers, and other areas concerning development.

3. Speeding Drug Development Processes

The development timelines, depending on the complexity of the product can range up to more than 10 years or less; which isn’t entirely sufficient to address the issues at hand. With AI technology, the lengthy span of 7 to 10 years has been reduced to 5 minimum. Advancements in AI and machine learning has truly changed the overall product development timeline. Moreover, the scientists are integrating the data, lab data, and other areas of inputs in combination to create an umbrella approach to drug development.

AI and machine learning is assisting the concerned personnel in making better and faster decisions resulting in acceleration of product development.

4. Optimizing Submission Dates Assisted by Machine Learning and Predictive Analysis

I think this fact has been maintained that AI has truly banked on the predictive algorithm, making its technology highest in demand by every industry. Moreover, it has been making waves and disrupting majority of businesses and their respective industries, as we speak.

With that being said, life sciences companies are responsible and accountable for the information they provide regarding safety on their products.  Pharma labels are important when it comes to communicating safety information to their consumers. These labels should be updated with the most accurate details and latest.

For this, AI and machine learning has been making it possible for the companies to determine optimal submission dates through connection of appropriate data points coupled with predictive analytics, determining the various dates including the expiry.

5. Improving Clinical Selection Reducing Trial Timelines

When candidates are being shortlisted for the clinical trials, we have seen that more than 80 percent of the clinical trials fail to meet the target of patient enrollment. This is owing to the extensive selection process backed by structured and unstructured data that required proper sorting and analysis to determine the best candidate. AI has optimized this process by a large margin and you can expect improved and accelerated clinical site and patient selection decisions, ensuring the deadlines are met.

Moreover, you also get help with making real-time course corrections that in result increases the likelihood of meeting the patient enrollment timelines.

That’s a Wrap

AI has been one of the strongest players in revamping the entire landscapes of various industries that includes the life sciences as well. It is not just the life sciences companies but even the other relevant businesses like the top life sciences consulting firms have been taking full advantage of AI and improving their services towards the concerned companies.

Author Bio: Paul M Stevens is known for his digital marketing experience, but more so, for his passion to convey the right message through effective content marketing and copywriting skills. If you do not find him engrossed in his elaborate campaigns, then he is penning down his thoughts and research in a rough draft. Currently, he has undertaken a life sciences consulting firm and working towards its growth in a digital space.