4 Ways Clinical Development Will Improve with Gen AI

1. Streamlining Research Processes with Semantic Search

Generative AI has the potential to revolutionize clinical and scientific research processes. Traditionally, research teams begin with manual keyword searches and sift through hundreds of articles across various sources. With generative AI-enabled tools, researchers can prompt the AI to rapidly search, gather, and distill relevant articles or even suggest unexpected information pathways to explore. This allows researchers to focus more on analysis rather than manual data collection.

For instance, if a company is developing a cholesterol medication, instead of manually searching for terms like “cholesterol,” “clinical trial,” “efficacy,” “safety,” “hyperlipidemia,” and “statins,” researchers can state their goal to the AI tool and receive contextualized reference materials to support their specific concept. This not only saves time but also provides comprehensive insights quickly.

2. Automating Clinical Trial Protocols

Writing clinical trial protocols can take anywhere from a few months to over a year. Generative AI and large language models (LLMs) can dramatically reduce this time to days or even hours. AI-enabled tools can scan thousands of entries within a given database, identify relevant patterns, and design baseline studies. These tools can draft eligibility criteria, define narratives, and provide other essential details for clinical trial protocols.

Additionally, generative AI can expedite protocol review and approval cycles by summarizing protocols and providing explanations. This can potentially reduce the time spent writing protocols by 50-60% and decrease related review cycles by another 10-20%.

3. Enhancing Clinical Study Reports

Analyzing clinical trial data and summarizing it in a way that demonstrates statistical significance, safety, and efficacy standards is time-consuming and prone to error when done manually. Generative AI can accelerate this process by revealing new connections not immediately apparent to human researchers or conventional models.

For example, traditional AI might extract a key statistic like trial recruitment velocity. Generative AI can explain the reason behind that number and offer recommendations for improvement. This additional context can include suggestions such as increasing social media promotion budgets or refining target audiences to accelerate enrollment.

4. Expediting Treatment Launch Processes

Once approval for a new therapy is secured in a primary market, a tremendous amount of work goes into launching the drug in secondary markets. Generative AI can automate much of this activity, including strategy development, market research, contracting, pharmacy benefit manager (PBM) negotiations, agency engagement, content creation, and material development.

Generative AI-enabled tools can also translate existing materials to reflect local language and culture, shedding an additional year from the go-to-market timeline and dramatically reducing content creation and design costs across various markets.

Conclusion

Generative AI is poised to play a significant role in the future of the life sciences industry. While the technology and regulatory landscapes are still evolving, companies that take a strategic approach to adopting and scaling generative AI today will likely gain a strong competitive advantage. By fostering a culture of AI literacy, building robust partnership ecosystems, establishing governance, and planning for adaptability, life sciences companies can unlock the full potential of generative AI in clinical development.

To learn more about how generative AI can transform clinical development, visit Cognizant’s Generative AI and Life Sciences sections on our website.

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