The use of large language models (LLMs) such as ChatGPT will be key in enhancing clinical trial management according to Endpoint Clinical’s Senior Director of Solutions Consulting, Chris Varner.
During a presentation at the 17th Annual Clinical Trial Supply West Coast 2024 conference in San Francisco, Varner, discussed how tools such as ChatGPT are transforming the landscape of Interactive Response Technology (IRT).
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In IRT, machine learning algorithms can be used to predict drug demand, optimise supply chains and reduce wastage. With ChatGPT, the platform can provide context-aware responses.
“It can understand the nuances of your questions, maintain a conversation flow, and deliver specific answers tailored to your needs. For instance, in the context of IRT, you might use ChatGPT to ask detailed questions about patient randomisation processes, and it would provide precise, actionable insights,” said Varner.
“Another advantage of ChatGPT is its ability to handle follow-up questions seamlessly. If you’re troubleshooting an issue or refining a process, you can engage in a back-and-forth dialogue with ChatGPT, allowing for a more efficient resolution than sifting through multiple search results.”
One of the more significant benefits is the ability to use templates and automation, he added. “For example, you can create pre-designed workflows for common IRT processes, such as managing patient randomisation or drug supply logistics. These templates can be customised for different trials, ensuring consistency and saving time.”
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By GlobalDataLLMs are also expected to play a crucial role in detecting data anomalies. The models can be trained to recognise patterns and identify outliers in large datasets, providing an additional layer of scrutiny to ensure data integrity. This capability could be especially valuable in clinical trials, where early detection of discrepancies can prevent costly errors down the line. They may also have an impact in suggesting and implementing UI/UX improvements, Varner added.
“By analysing user interactions and feedback, LLMs can provide actionable insights into how trial management platforms can be made more user-friendly and efficient, ultimately enhancing the overall user experience,” Varner said.
For randomisation, LLMs can optimise the process by analysing complex datasets and ensuring that patient assignments are both fair and effective. “This is crucial in maintaining the scientific validity of a trial while also managing logistics more efficiently,” he explained.
LLMs may also offer potential in external integration. Acting as a bridge between different systems, facilitating smoother data exchange and collaboration between various stakeholders in a trial. LLMs could also ensure that data flows seamlessly, whether integrating external databases, patient management systems, or regulatory platforms.
Overall, clinical study designs can be revolutionised by LLMs, Varner said. By analysing historical data and simulating various scenarios, the models can help design more robust and adaptive trials that are better suited to meet the objectives while minimising risks.
“As we look to the future, it’s clear that the strategic implementation of LLMs in IRT will drive significant improvements across all aspects of clinical trial management, from planning and execution to data analysis and reporting,” concluded Varner.
