From Data to Discovery: Generative AI for Research in Life Sciences

In the life sciences industry, comprehensive background research is crucial for groundbreaking discoveries, especially in the realm of clinical trials. These trials, conducted in multiple phases to assess the efficacy and safety of new treatments, depend heavily on thorough background research. Traditionally, researchers meticulously review scientific literature to gather relevant information, identify trends, understand past outcomes, explore reasons for trial terminations, and analyze demographic profiles of participants. This exhaustive process, while essential, can be cumbersome and time-consuming.

Enter Generative AI, a transformative technology that is revolutionizing background research in life sciences. By leveraging AI, researchers can enhance efficiency, broaden exploration, and uncover novel research opportunities. Specifically for clinical trials, Generative AI streamlines the analysis of previous trial data, provides deeper insights into demographic trends, and identifies potential reasons for past trial failures. This leads to more informed decision-making, improved trial design, and ultimately, more successful outcomes in developing new treatments.

The Importance of Background Research

Background research is the bedrock of scientific inquiry. A comprehensive literature review not only helps researchers understand the current state of knowledge in a particular field but also identifies gaps, leading to the formulation of pertinent research questions and the design of effective studies. For instance, a thorough review can reveal prevailing theories, conflicting findings, and methodologies used in previous studies, providing a robust framework upon which new research can be built.

Consider the development of CRISPR-Cas9, a revolutionary gene-editing technology. The foundational research for CRISPR involved extensive literature reviews of bacterial immune systems. By understanding how bacteria use CRISPR to fend off viruses, researchers were able to repurpose this system for gene editing in higher organisms. This breakthrough was facilitated by a deep understanding of existing biological mechanisms, underscoring the importance of meticulous background research.

Another example is the rapid development of COVID-19 vaccines. Researchers relied on decades of literature on Background research was crucial in identifying the spike protein of SARS-CoV-2 as a target for the vaccine, leading to the unprecedentedly swift development and deployment of effective vaccines.

Challenges of Traditional Literature Reviews

Despite its significance, traditional literature review methods present several challenges. Firstly, manual literature searches are incredibly . Researchers often spend weeks or months poring over databases, reading abstracts, and going through articles to compile relevant information. This labor-intensive process can delay the onset of actual experimental work and slow down the overall pace of scientific discovery.

Moreover, keyword-based searches, a common method in traditional literature reviews, have inherent limitations. They often yield an overwhelming number of results, many of which may be irrelevant. Additionally, these searches can miss important studies that use different terminologies or are published in less accessible journals. This can introduce biases and potentially skew the understanding of a research topic.

According to a study, the average R&D efficiency of major pharmaceutical companies is $6.16 billion in total R&D expenditures per new drug. Additionally, nearly 50% of these companies fail to achieve positive R&D productivity, highlighting significant inefficiencies in the drug development process.

Gen AI-powered Background Research

Generative AI addresses these challenges by automating the processes, including identifying. Gen AI-powered research systems can conduct context-aware searches, identify relevant studies, synthesize information, and generate summaries, significantly streamlining the literature review process.

Conducting Context-aware Searches

Unlike traditional keyword-based searches, Gen AI leverages natural language processing (NLP) to understand the context and intent behind research queries. This allows it to retrieve more relevant articles and reduce the noise typically associated with manual searches. For example, a researcher investigating the effects of a specific protein on cancer metastasis can rely on Gen AI to identify pertinent studies across various subfields, including molecular biology, oncology, and pharmacology, ensuring a comprehensive overview of the topic.

Synthesizing Information and Generating Summaries

One of the most powerful features of Gen AI is its ability to synthesize large volumes of information and generate concise summaries. This capability is particularly valuable in the clinical research realm of the , where the body of literature is vast and continuously expanding. Gen AI can scan thousands of articles, extract key findings, and present them in a coherent summary, saving researchers countless hours of reading and analysis. 

Highlighting Knowledge Gaps and Suggesting Research Directions

Gen AI goes beyond merely summarizing existing studies; it can also identify gaps in the current knowledge and suggest potential research directions. By analyzing trends and detecting inconsistencies in the literature, Gen AI can highlight areas that warrant further investigation. This proactive approach helps researchers formulate novel hypotheses and design studies that address unmet needs in the field.

For instance, Gen AI has been used to analyze cancer research literature and identify underexplored areas such as the role of the microbiome in cancer progression. Gen AI-powered literature reviews highlight these gaps, enabling researchers to pioneer new areas of study and potentially discover groundbreaking treatments.

Benefits of Gen AI in Background Research

The integration of Gen AI in background research offers several significant benefits:

Increased Efficiency and Focus on Analysis: By automating the labor-intensive aspects of literature reviews, Gen AI allows researchers to focus more on analysis and interpretation. This accelerates the research process and enables scientists to dedicate more time to experimental work and innovation.

Broader and Deeper Exploration: Gen AI facilitates a more extensive and in-depth exploration of scientific literature. Its ability to cross-reference including open-access materials, internal repositories, and various knowledge bases, ensures a more holistic understanding of research topics.

Identification of Novel Research Opportunities: By uncovering gaps and suggesting new research directions, Gen AI empowers researchers to explore uncharted territories and drive scientific innovation. This capability is crucial in fields like , where the pace of discovery is rapid, and the potential for breakthroughs is immense. Moreover, it accelerates clinical research by identifying relevant prior studies, patient populations, and outcomes. This multifaceted support extends to monitoring ongoing trials, ensuring researchers stay updated with the latest literature and regulatory guidelines.

Accelerated Documentation: Generative AI expedites the creation and updating of various types of documentation, such as clinical study reports, investigator brochures, and study protocols, where thorough background research is essential. This acceleration not only enhances the efficiency of the documentation process but also ensures that all relevant and up-to-date information is included, improving the overall quality and reliability of these critical documents.

Top Life Sciences Companies Leveraging Generative AI

Here are some examples of how major players in the industry are utilizing Gen AI:

Merck and Co.: Merck & Co. is presently investigating the use of Variational AI’s Gen AI platform to enhance their background research processes for accelerated drug discovery. This involves extensive analysis of historical biological data, thorough analysis of past literature reviews and predictive modeling to identify promising candidates for further development.

AstraZeneca: AstraZeneca has invested $247 million in a partnership with Absci Corp to accelerate cancer antibody drug discovery using Gen AI. This partnership underscores the role of generative AI in enabling extensive background research, enhancing the precision and speed of identifying effective therapeutic candidates.

Novartis: Novartis has strategically invested in Yseop, a prominent Gen AI firm specializing in clinical trial writing, enhancing its capabilities in producing extensive healthcare content, scientific papers, disease and drug-related documents and regulatory reports after thorough background research. This partnership underscores Novartis’ commitment to leveraging advanced Gen AI technologies to drive innovation and efficiency in medical writing and regulatory compliance.

Pfizer: Pfizer is currently in the process of launching its proprietary Generative AI marketing platform called “Charlie” which aids in content creation, legal reviews, strategy formulation, and analytics. It leverages Pfizer’s extensive data across treatment categories and products to tailor messages through segmentation models and is bolstered by a custom ChatGPT and recommendation algorithms, highlighting the importance of accelerated contextual research.

These examples illustrate the significant impact of Gen AI in reducing research time and enhancing the efficiency of drug discovery and development in the life sciences sector. It also highlights the importance of extensive background research in not only accelerating drug discovery, but also in automating clinical trial documentation, regulatory compliance and personalized content creation for faster commercial and medical outreach.

Hexaware’s Clinical Co-pilot

Hexaware’s clinical co-pilot leverages generative AI to significantly expedite the clinical research process. This innovative tool is designed to:

  • Summarize Essential Information: By rapidly synthesizing vast amounts of clinical data, Clinical Co-pilot provides concise and accurate summaries that save researchers considerable time and effort.
  • Deliver Valuable Insights: Utilizing advanced AI algorithms, the tool extracts key insights from complex datasets, aiding researchers in identifying critical patterns and trends.
  • Enhance Research Efficiency: By automating routine tasks and reducing the time required for literature reviews and data analysis, clinical co-pilot enables researchers to focus on high-value activities.


Generative AI is transforming the landscape of background research in life sciences, offering unprecedented efficiency, depth, and breadth in literature reviews. By automating context-aware searches, synthesizing information, and identifying knowledge gaps, Gen AI empowers researchers to ask more insightful questions and drive innovation. As exemplified by its application in fields like oncology and genomics, Gen AI not only accelerates the research process but also uncovers novel research opportunities, paving the way for breakthroughs in life sciences. In this context, Hexaware’s clinical co-pilot emerges not merely as a tool but as an essential ally in background research. For more information on how clinical co-pilot can transform your research processes, contact us at:

About the Author

Meghna Mukherjee

Meghna Mukherjee

Management Trainee (Life Sciences & Healthcare Practice)

Meghna holds a PGDM in Marketing and Analytics from the Great Lakes Institute of Management, Gurgaon, and a B.Tech in Biotechnology from the Heritage Institute of Technology, Kolkata. She has gained valuable experience through internships at MongoDB, CHIS, and NIT Durgapur, sharpening her skills in business development, social media marketing, and research. Her interests include market research, marketing analytics, Generative AI, Power BI, Tableau, and content writing.

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