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.
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:
Gain in-depth insights into how GenAI can be used to enable faster, more efficient drug development processes.
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.
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Endnotes:
1Scout Clinical, “Understanding clinical trial patient attrition: Causes & impact on research success,” November 2023.
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