How Is AI Reshaping Research To Benefit Society And Change the Landscape Of Scientific Communication?

With the advent of AI, passionate entrepreneurs and even researchers themselves started solving for the research industry and specifically for improving scientific communication

By Nishchay Shah

Nishchay Shah (CTO, Cactus Communications)

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Imagine if the results of a clinical trial for a groundbreaking pharmaceutical are not published accurately for the research community to validate the efficacy of the new drug. It would leave much room for uncertainty and undermine the importance of the research. What if statisticians send out a report which forecasts the next big financial crisis, but do not talk about the methodology and source data used for deriving this conclusion, and that report is (mis)quoted by large news channels? The way research gets documented – in print media and online – impacts the current and future advancements in that field.

In simple terms, scientific communication is an important process by which the discoveries, observations and thoughts of the scientific and research communities are relayed back to all of humanity, sometimes in a disseminated form, sometimes as is for critical review and comments from their peers across the globe. Globally, researchers now prefer Twitter and LinkedIn over other social media platforms, as engagement on these platforms leads to successful collaborations, increased funding, and award nomination possibilities. Some other common modes of communicating science include research papers, dissertations and scientific journals.

Effective communication is key for researchers. Being able to disseminate their findings to the community and showcase their work is important. To ensure that good research doesn't get sidelined, researchers rely on freelance writers and companies providing services around writing, editing, and proofreading raw manuscripts. The classic dilemma faced by a first-generation entrepreneur, that is, whether to build tech in-house or outsource it, is often found in different variants across industries. For researchers and research labs, writing and editing in-house vs outsourcing becomes a major decision that can influence their ability to scale their research output and increase their communication with the world.

The paradign shift

Automated summarization, context-driven personalized research paper search, and tools for creating visual abstracts and graphics didn't exist at all a decade ago. With the advent of AI, passionate entrepreneurs and even researchers themselves started solving for the research industry and specifically for improving scientific communication. With the stable adoption of technology and products in this domain, scientific research is benefitting immensely.

We talk about AI as a new technology, but it has been a part of our daily lives since ages, in the form of different software and products. Some consider AI to be a smart algorithm or magic sauce, while some confuse data science and analytics with AI. In general, AI can now be defined as anything that is driven by data and is smart enough to give better results than existing technical solutions. AI solves by bringing a missing piece of the puzzle to the table, but it still is never the complete solution.

The scientific research community provides a great example of how the acceptance of technological advancement can lead to minimizing effort and maximizing delivery (in this case, of research findings) to the end audience; thereby strengthening the role of AI.

During the early stage of the COVID-19 pandemic, researchers and medical practitioners were working around the clock to get a vaccine out. With access to very few samples and limited data, it was difficult for them to rapidly test and experiment. The chief AI officer at one of the pharma companies, which launched the first generation of COVID vaccines, even mentioned that one of their biggest bottlenecks was the manual generation of mRNA sequences. With the right amount of process automation and AI algorithms, they were able to automate this generation process and scale to nearly 30 times with better consistency. This helped them quickly productionize and significantly reduce the time to market for a vaccine from an average 5-10 years to less than one year.

AI as an innovator

We are living in an era where data is the new electricity. So much of it is generated on a daily basis. The same is also true for research. To provide some context, millions of research papers are published each year across all subject areas. Researchers face three major hurdles: searching for the right paper, accessing the paper, and spending time searching, accessing, and reading the paper only to find that it is irrelevant to them.

With the help of smart research paper fingerprinting based on extracting concepts and relevant context, and by using advanced algorithms such as Concept Mesh and Neural Extractive Search, the search for relevant papers becomes much more efficient. These algorithms are much better than the traditional keyword-centric search.

For making research accessible to everyone, Open Access (OA) initiatives powered by strong data warehousing and AI strategies adopted by publishers are now driving various use cases in improving scientific communication. Auto-summarization and Information Retrieval algorithms benefit immensely because of OA, which in turn helps in overcoming the third hurdle – reducing the time spent by researchers to go through hundreds of research papers.

AI as a creator

We are living in the age of disruption, especially in the art domain where recent advances made by generative AI models like DALL-E and Stable Diffusion have the power to create phenomenal visualizations with just simple text prompts (written ideas). Taking it one step ahead, imagine using such models to generate visual abstracts, infographics, and posters, which can distill your research into consumable content for the masses.

Not only this, but transformer-based large language model products help people and researchers, especially from a non-native English-speaking background, write with negligible errors, and give them tips to become better. They also help reduce the rejection rate of research papers, by helping minimize grammatical errors and adhere to the many micro standards of different publishers.

Nishchay Shah is CTO at Cactus Communications

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