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How to Leverage Generative AI Today to Accelerate Your Clinical Trials

Mar 12, 2024 - 3 min read
How to Leverage Generative AI Today to Accelerate Your Clinical Trials

In 2024, generative AI continues to rapidly become a global buzzword, and we’re seeing more industries quickly adopt this technology. But for many, a lack of understanding the capabilities and nuances of gen AI have hindered adoption—despite its enormous potential.

The healthcare and life sciences industry is one area where generative AI is poised to make the biggest impact. “The McKinsey Global Institute (MGI) has estimated that the technology could generate $60 billion to $110 billion a year in economic value for the pharma and medical-product industries, largely because it can boost productivity by accelerating the process of identifying compounds for possible new drugs, speeding their development and approval, and improving the way they are marketed” (McKinsey, 2024). 

One of the single most impactful applications that life sciences can leverage today is the use of synthetic data—high-fidelity data created by algorithms that protects patient privacy. Synthetic data itself has countless applications, including:

Improving Protocol Design

Improving protocol design with synthetic data and generative, predictive modeling can help organizations make better, faster decisions—shortening trial timelines while also improving the probability of trial success. By leveraging synthetic data, organizations can simulate outcomes for distinct patient subpopulations based on past trials in similar drug classes to help predict patient outcomes, as well as refine the parameters of a given trial. Designing more robust and adaptable clinical trial protocols with generative AI can allow for safer, more efficient trials—ultimately increasing the likelihood of a trial's success. 

In addition to creating safer and more efficient trials, synthetic data and generative modeling can help improve endpoint adjudication—a process where endpoints are judged in an unbiased way in an effort to increase data integrity and accuracy, and eliminate potential biases within a study. These synthetic datasets can help streamline this process in an effort to speed up decision making and improve overall clinical trial efficiency. 

Augmenting Data

Augmenting data, or enhancing existing clinical trial datasets, can increase model robustness and generalization and create a more balanced dataset. By using synthetic datasets to augment their existing data, organizations can effectively "upsample" under-represented groups within their trials, providing a more comprehensive and diverse representation of the real-world population. This approach lets researchers design improved protocols and generate evidence that better reflects the broader demographic landscape.

By addressing the limitations of skewed participant demographics, companies can enhance the generalizability of their findings, leading to more robust conclusions and increasing the overall validity and applicability of the clinical trial results to a wider range of individuals. Additionally, synthetic data can help pharma companies gain the necessary insights from their existing trial data rather than spend resources trying to find the right sites and patients—a task that can often cause trials to slow or fail. 

Overcoming Data Privacy Concerns

Overcoming data privacy concerns that have traditionally hindered the sharing of cross-sponsor historical clinical trial data. While trial data is one of the richest sources of insights to inform future clinical development, the sharing of this data is severely limited because it’s crucial to maintain patient privacy while still maintaining the integrity of the data in any given clinical trial. Providing personal health information is something patients are often hesitant about, and rightfully so. 

Early adopters and healthcare execs have already begun to make it clear that generative AI in a healthcare setting must have robust techniques and safeguards in place to ensure patient privacy (Fierce Healthcare, 2023). Synthetic datasets have the ability to address many of the concerns surrounding patient privacy and data integrity. These datasets ensure patient anonymity, while keeping their data safe and also preserving the integrity of the clinical trial dataset. 

Generative AI in the healthcare and pharma industries has only just begun to take off, and while there are still many questions and hesitations, it’s proving to be a tool that can benefit and address primary concerns for both patients and businesses. Gen AI is expected to expand even further in 2024, and can be expected to be seen across a variety of use cases (Fierce Healthcare, 2023). 

While full adoption has yet to happen, the potential economic benefits of generative AI look promising—although this must come with further development of AI tools specifically for healthcare (Forbes, 2023). 

Introducing Simulants

Medidata AI’s fit-for-purpose solution, Simulants, is a synthetic data tool designed to access sensitive, patient data by preserving the characteristics and fidelity of a dataset while maintaining patient privacy. Generated from historical clinical trial datasets, clinical developers can leverage Simulants to glean insights that may otherwise be impossible to discover while maintaining data privacy.

Learn more about Simulants.

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