Accelerating clinical development with Generative AI

As life sciences companies face the challenge of managing flexible, adaptable, and cost-effective trials, the integration of Generative AI (GenAI) may be a game-changer. Learn how GenAI in clinical trials can enhance operational efficiencies and help improve patient outcomes.

Unlocking new growth potential in clinical trials with GenAI

GenAI offers life sciences leaders a powerful tool to help streamline and enhance various stages of clinical trials. By automating key, repetitive tasks such as document generation and regulatory submissions, the technology can reduce overall cycle time and costs. Potential positive outcomes of GenAI in clinical trials include:

• Improving study startup. Increase velocity by automating document generation activities. Working from previous examples of clinical trial protocols, site contracting agreements, and clinical report forms, biopharma organizations can quickly draft and refine the essential documentation required to establish new test sites. This can help foster diversity in patient cohorts and speed up site creation, reducing effort and costs.
• Revolutionizing patient engagement and retention. Increase study retention by amplifying patient engagement. GenAI can personalize outreach, identify key doctors and locations, and answer patient questions, reducing early trial dropouts. This can cut costs significantly, as patient attrition can be as expensive as $20,000 per participant.¹
• Streamlining regulatory submissions. Improve regulatory engagement with tailored submissions. GenAI enables faster identification of relevant materials and automated document tagging, making it easier for clinical teams to assemble comprehensive dossiers for regulators.

Generative AI to accelerate clinical development

Gain in-depth insights into how GenAI can be used to enable faster, more efficient drug development processes.

Key considerations for GenAI in clinical trials

Many life sciences leaders are excited by the potential of applying GenAI to clinical trials. However, some are understandably concerned about managing risks to quality and employee experience. Consider the following when developing your strategy.

Recognize the difference between a task and a job

As GenAI use cases grow, many are concerned there will be an equal and opposite reduction in human workers. While a valid concern, leaders must clearly differentiate between a task (i.e., an activity one performs as part of their work) and a job, which has a far greater scope. Clinical development is made up of a variety of tasks ripe for automation: repetitive, manual, and rule-based activities. The jobs attached to them, however, remain critical.

Anticipate a shift toward more and more specialized knowledge

As discrete tasks are increasingly shifted toward GenAI, organizations will need to stand up the proper guardrails to ensure the integrity of the outputs. GenAI will increasingly drive content creation (e.g., outreach to trial participants, plain language summaries of clinical data) and, as a result, humans will need to validate those outputs.

Be cautious of historical data

Clinical development has faced challenges related to creating diverse clinical trial cohorts. For that reason, leaders in research and development (R&D) should be careful not to over-index on historical clinical data, or they risk amplifying biases inherent in existing datasets. Moreover, ensuring that trustworthy AI frameworks and governance approaches are in place will help mitigate potential for bias and unintended outcomes.

Integrating GenAI into your clinical trials

We understand that effectively integrating AI and GenAI across your life sciences enterprise can seem daunting. Our teams are here to help your business fuel innovation to lead in your industry.

If you’d like to learn more about how AI is revolutionizing drug development in the life sciences industry, let’s set up a conversation.