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COVID-19: Rewriting the Use of AI in Life Sciences

In 2020, pharma companies pivoted from slow and painstaking processes to speed and agility that developed life-saving vaccines and treatments in less than a year. This is a process that historically has taken the better part of a decade. Those deep in the throes of drug development are seeing value in technology that can help speed the drug application process safely and effectively, and the industry is shifting.

In a 2021 KPMG research study of life sciences business leaders, 81 percent said they wished their business would more aggressively adopt AI technology. 

The use cases are compelling, specifically during the Clinical Study Report (CSR) phase of drug development. After the drug or vaccine has been successfully tested, the road to regulatory approval must pass through the CSR. Medical writers invest weeks—sometimes months—into writing CSRs that explain in detail the methods of clinical trials and their results. These long, complex scientific reports require a team of highly educated medical writers and command a pivotal role in the decisions that regulators will make regarding whether a drug goes to market.

Much of the work during the CSR is very manual, repetitive, and data focused, yet with the help of AI parts of the process can be automated. It may seem like a no-brainer, to offload the tedious parts of report writing to an augmented solution to speed up the regulatory process. However, cultural barriers to change and resistance to augmentation are still prominent within life sciences companies. Convincing teams, researchers, and scientists to have faith in AI and replace their current workflow with a new process can be a difficult obstacle to overcome. 

Opportunity for AI and the Rise of NLG: 

AI technology has been around for years - systems that mimic cognitive capabilities, like reading patterns, predictive learning, and solving problems. But many companies need to take AI a step further and implement systems that translate data into human language. The missing piece of many augmented workforces is natural language generation (NLG). NLG is software that writes like a human being from structured data, generating a narrative instantly. Applying NLG to structured data from clinical trials in order to create written narratives reduces the burden on medical writers across the world by equipping them with the tools to create parts of the CSR automatically. 

For a task like converting 30 tables into data for 20 pages of a CSR, medical writers need hours to review the data, process it, and convert it to words on a page. By implementing NLG technology, teams of medical writers can realize a 30% savings of time. NLG is process streamlining and a way to gain time in writing and reviewing. AI cannot tell the medical writer what to write, but AI helps put research into words—analyzing the data first and then generating natural language to explain it, thus allowing the medical writer to focus on more strategic parts of the CSR.

Over the last two years, the life sciences industry has demonstrated the sheer power of human ingenuity and willpower, and science has been pushed further and faster than ever before. AI is poised to create change in the pharmaceutical industry. AI-powered algorithms can now conduct tasks that once required human intelligence to complete. The pandemic has solidified AI’s contribution to the future of pharma–driven by the race to find a vaccine to slow and stop the spread of the coronavirus. 

Download our latest whitepaper to learn more about the Impact of Covid-19 on Life Sciences: The Rise of AI and Natural Language Generation Technology here