Data management
PhD, head, predictive analytics, Pfizer Research & Development, OARS. As the healthcare industry pivots to a more data-driven model, generative AI emerges as a transformative tool, offering solutions for a more efficient, cost-effective and patient-focused approach. This technology enables companies to simulate and predict various aspects of clinical trials, such as identifying the appropriate patient population, managing protocol complexity or time forecasting of the clinical trial delivery process. Crowther emphasises the major role generative AI plays in managing protocols: “We know that our protocols are getting more complex over time as an industry as we get more focused on personalised medicine and oncology, so being able to map and identify efficiencies and simplification of some of the protocols to delivery to the patient is really key.” The integration of generative AI in clinical trials has the potential to streamline these processes, effectively reducing the burden of “backend” tasks to concentrate on more meaningful work. While the pharmaceutical industry is increasingly investing in relevant real-world data with the aim of enhancing research and patient care, the potential of this data can only be realised if it is effectively connected and leveraged across the industry. This is where generative AI, highlights Crowther, can play a massive role behind the scenes. “It’s not the sexy science, it’s the real stuff that needs to happen,” he explains. “Essentially it helps us label, connect and actually leverage all of that data.” Through retrieval augmented generation (RAG), generative AI can process and connect vast amounts of information from massive silos of data that would usually remain underutilised due to the sheer volume and complexity of data. By using contextual intelligence, generative AI can link seemingly unrelated data points to provide a comprehensive understanding of trends and patterns that would go unnoticed. “Generative AI is the solution we have been looking for to break down silos across the pharmaceutical industry,” asserts Crowther. With the global data lake expected to reach 180 zettabytes by 2025, according to Crowther, and roughly 40% of that healthcare-related, the pharmaceutical industry is sitting on a goldmine of information. This data, encompassing everything from patient records to clinical trial results, represents an enormous opportunity to advance medical research and improve patient outcomes. But to fully capitalise on this, it’s crucial to go beyond data collection and invest in tools that can make sense of it all. Generative AI is one such tool that can visualise and interpret this data to transform raw information into actionable insights. Crowther believes that the integration of generative AI will provide “new ways and new optics or angles to look at the data that we have and how that can be applied within our sphere”.
Clinical Trials Insight /
www.worldpharmaceuticals.net
Generative AI’s potential extends far beyond merely connecting and leveraging vast datasets, however; it also plays a crucial role in transforming the pharmaceutical industry into a more data-driven and data-savvy sector. As Crowther emphasises, “It’s really about lowering the barrier to entry for data literacy for our teams to be able to leverage that to build better solutions [and] help them build better strategies.” By making data more accessible and understandable to all members of the team, generative AI empowers healthcare professionals to utilise this information effectively, leading to improved decision-making and strategic planning. Crowther also notes that, for sponsors, the proper implementation of generative AI can significantly reduce the administrative burden associated with clinical trials. This includes time-consuming tasks of surveys and extensive documentation. By streamlining these processes, clinical sites can shift their focus to more impactful activities to improve the quality of patient care and engagement, such as building rapport with patients. This improved interaction is critical in ensuring that patients are more comfortable and engaged in their treatment processes. It also allows healthcare providers to spend more time assessing the appropriateness and effectiveness of treatments, ensuring that the right drugs are used for the right patients, in the right contexts, and at the right times.
Bias and privacy concerns While the potential of generative AI in clinical trials is vast, it also raises significant ethical and regulatory challenges that must be carefully navigated. Crowther underscores several critical ethical concerns, starting with the issue of bias. “We have all that data, but there can also be bias in that,” he points out. For example, has there been an overemphasis on certain patient demographics at the expense of others? Crowther highlights this as a key concern: “How do we identify early datasets that might have bias and how do we remove that bias from our analysis and our predictions.” This issue is pressing because the use of historical data to build clinical models can perpetuate existing biases, leading to skewed results and potentially inequitable outcomes in clinical trials. To mitigate bias, it’s crucial to employ tools and methodologies that detect and correct these issues early in the data analysis process. One such tool is model drift, which refers to the gradual change in model performance over time as new data is collected. This concept is essential for maintaining the accuracy and fairness of AI models in clinical trials. By continuously monitoring and adjusting for model drift, researchers can ensure that their models remain relevant and unbiased, reflecting the latest and most diverse data available.
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