AI in Pharmaceutical Research: The Case for Automation

AI in Pharmaceutical Research: The Case for Automation

Within the field of pharmaceutical research, there are an overwhelming number of variables to consider within research and development (R&D) as well as production and manufacturing. 

Between these considerations, navigating government and industry regulations, existing and impending medical patents, and staying ahead of competitors while keeping abreast of current trends that can impact trial results and outcomes make manual research a logistical nightmare for CI professionals and their teams. 

On top of this, the fight to find the data necessary to make swift, strategic decisions means it’s even harder for C-suite executives to ensure that R&D budgets stay on track. Projects extend beyond deadlines due to the never-ending snowball effect of vast new sets of incoming data and information that impact research and clinical trials and testing.

AI is reshaping the way data is curated and mined for insights

With such an overwhelming abundance of unstructured and unusable data that needs to be identified, extracted, and analyzed, it’s unfortunately far too easy for invaluable insights to slip through the cracks, setting teams back and potentially costing businesses millions in lost revenue.

The only scalable solution for efficiently managing vast amounts of incoming large-scale datasets is to leverage the power of AI and machine learning to streamline and automate the research process. 

Today, we’ll be making the case for why AI and automation should be front and center in your research strategy to speed up, simplify and reduce the costs of your research process, no matter your organization’s size or scale.

AI’s competitive edge in research capabilities

AI-powered systems have the ability to rapidly collect, analyze, interpret and huge volumes of incoming datasets, presenting them as accessible, actionable insights that enable swift and powerful decision-making. 

AI’s ability to trump human input when it comes to research and analysis output is not anecdotal or circumstantial. In a recent case study by Hubspot, researchers examined the effects of Intelligent Literature Monitoring (ILM) which augments literature searches with AI and Natural Language Processing (NLP) capabilities compared to a controlled study that was manually conducted. 

Compared to the manual research process results, AI-assisted ILM reduced research time by between 88% and 92%, while still achieving 99.8% sensitivity and 95% specificity. 

By utilizing AI and machine learning to power their research operations, CI professionals and teams can transform the way they conduct research, resulting in efficiency gains, greater research accuracy, and reduced expenses that ease strained R&D budgets. 

AI systems can quickly and efficiently mine enormous amounts of data from various medical publications, articles, press releases, research papers, and other sources. Using deep learning, it can accurately interpret both printed and handwritten text as well as chemical representations and figures, test results, scans, and imaging to glean essential data while filtering out unimportant data, presenting it as accessible insights.

Research teams no longer have to spend endless hours manually researching and interpreting relevant data to inform development strategies. 

With the data they need at their fingertips, they can reinvest this recovered time into strategic planning and modeling, such as identifying new business opportunities, managing potential risks to pipeline products, and analyzing competitor actions, all of which translates into more business revenue and allow for better budget guard railing.  

AI as a scalable research solution

Thanks to a complex interplay of 2020’s global recession coupled with ongoing rising inflation and curbed consumer spending, 2022 has seen a slew of layoffs across the tech sector. Companies looking to conserve their resources are increasingly streamlining and automating their operations where possible. 

A common misconception is that adopting AI and machine learning is a more costly investment decision as opposed to simply hiring more staff. While scaling your teams might seem like a more cost-effective strategy upfront, the ever-increasing data volumes they’ll be facing means that, for your teams to keep up, you’ll continuously need to invest in expanding your team, driving up overheads and stretching budgets.

AI is a viable alternative that is inherently scalable and self-sustaining, able to manage increasing data volumes without affecting its output, accuracy, and efficiency. Equipping your teams with a scalable research solution, as opposed to perpetually hiring additional staff members, solves both the underlying challenges of scale and cost.  

Similari: Your partner in research innovation

At Similari, we harness AI and deep learning capabilities to streamline and simplify your research operations to enable faster, more agile decision-making and proactive, instead of reactive, development strategies to keep you one step ahead of competitors. 

Our platform seamlessly tracks, consolidates, and analyzes enormous volumes of datasets in seconds to bring the data you need to you, eliminating the costly, time-consuming hunt for vital information necessary to take swift action, capitalize on opportunities and mitigate potential risks with ease.

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