How a data lakehouse can give you a panoramic view of your AI-enabled clinical trials 

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In recent years, the term “data lakehouse” has entered the lexicon of data professionals. For AI-enabled clinical trials, the lakehouse architecture promises seamless integration of diverse data streams, spanning patient health records to real-time sensor data, all processed efficiently and queried in structured formats.

The lakehouse architecture aims to provide a comprehensive overview of data, ensuring both vast storage and real-time processing capabilities. In other words, the lakehouse offers the “best of both worlds” when it comes to data warehouses and data lakes, according to Venu Mallarapu, vice president of global strategy and operations at eClinical Solutions.

AI and ML move from buzzwords to practical tools in clinical trial management

As the use of AI and ML in clinical trials becomes more prevalent in patient recruitment, real-time data monitoring and beyond, …

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How AI and the cloud can transform R&D workflows and fuel collaboration

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The rise of cloud-based systems and AI-assisted analysis has dramatically transformed the landscape of scientific research and collaboration. One striking example is the mRNA company Moderna, which used the cloud to develop and deliver its first clinical batch of a COVID-19 vaccine candidate for phase 1 trials in a mere 42 days after the initial viral sequencing​.

While enabling real-time international collaboration, this new paradigm has also introduced novel challenges. Simon Adar, CEO of Code Ocean, found the struggles of cross-geographical R&D collaboration during his PhD work at Cornell University. While file-sharing systems provided some relief, they fell short when it came to coordinating code, data, software and troubleshooting across different geographies. “It wasn’t enough, because when you have code and data, you also need all the software depe…

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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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Harnessing the untapped potential of legacy data in pharma R&D

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Clinical trials for a new therapy cost a median of $41,117 per patient. Costs like this are no surprise to pharma leaders. But during an age of increasing budgetary pressures, drug developers are under pressure to do more with less money and staff. While there are no “simple” answers to this challenge, there is one strategy that offers research and development (R&D) teams a very powerful approach: better leveraging existing legacy data. 

Pharmaceutical companies own petabytes of imaging data, generated by in-house research, investigator-initiated studies or clinical trials. This data is valuable and can yield insights that can help researchers better understand disease mechanisms and inform therapeutic approaches. But in many cases, researchers cannot access this important data, as it remains in silos with CROs, investigator labs, or within a specific research group…

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5 common data management problems affecting drug discovery

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Ask a pharma researcher how well they’re able to leverage their organization’s medical imaging data, and you might hear a discouraging response. While most pharma companies have massive amounts of clinical and medical imaging data, often, most of the imaging data isn’t ready for modern research processes and infrastructure. This imaging data is an untapped asset — it’s disorganized, difficult or impossible to query, not normalized and in no way ready for machine learning and AI. The result is innovation is slowed.

Imaging data is a rich source of information that can hold the key to many discoveries, but it is complex to work with. Pharma companies need a sophisticated data management infrastructure to help manage this complexity and seek to scale up their research.

Here are five common data management problems to consider as your organization evaluates its path forward.

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