8 considerations to boost clinical trial productivity with AI while dodging hallucination hurdles

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The development of new drugs is undeniably a data-intensive endeavor. Despite impressive advances in AI over the past years, researchers often continue to grapple with crushing data volumes. This hurdle is particularly apparent in clinical trials, where crucial data is often stored in machine-unfriendly formats such as PDFs, PowerPoint or HTML or other formats.

This article explores strategies to harness AI for data management in clinical trials while avoiding potential pitfalls such as data integrity issues and large language model hallucinations, which can lead to unreliable or distorted outputs.

1. Understand the complexity of clinical trial data

The complexity of clinical trial data can be difficult for someone outside the field to appreciate, according to Jeff Elton, CEO of Concert AI. “There can be 60 to 70 different levels of inclusion and exclusion criteria,̶…

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