How synthetic data accelerates oncology research and drug development 

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Synthetic data in oncology is transforming how researchers and developers approach real-world evidence. They often need this evidence to test hypotheses, predict outcomes and develop algorithms. But privacy constraints and access related to patient data can create delays and lengthen project timelines.

Oncology drug researchers and developers have recently begun using synthetic data in oncology to get around the privacy constraints and access issues related to patient data that create delays and lengthen project timelines.

Conceptually, synthetic data in oncology is about taking private patient information and enabling researchers to access the data without compromising privacy, offering a significant tool for current oncology research processes.

Traditional vs. new data approaches in oncology research

Traditionally, oncology researchers and drug developers have relied on t…

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A checklist for unlocking the promise of AI in clinical trials

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AI algorithms offer a myriad of advantages for clinical trials. AI techniques can, for instance, support patient enrollment and site selection, improve data quality and enhance patient outcomes. AI algorithms — combined with an effective digital infrastructure — can also help aggregate and manage clinical trial data in real time, as Deloitte has noted. Last week, a startup revealed an AI system that can accurately predict clinical trial outcomes.

Yet for organizations to fully realize AI’s promise is not simple. The task requires oversight, transparency and diverse collaboration. Core considerations include educating users to build trust in AI tools and ensuring the clinical precision of medical-grade AI algorithms. From unraveling the ‘black box’ of algorithms to safeguarding patient privacy, this article provides a checklist to help organizations responsibly incorporat…

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In data we trust: AI’s growing influence on drug development

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The journey to developing a successful drug, theoretically, may appear linear: you discover the right drug, find the suitable patient and administer it at the right time. The reality, however, often deviates from this straightforward path. Aligning these three variables remains notoriously difficult, often leading to elongated timelines strewn with failures, sometimes extending over a decade with costs often in the billion-dollar range.

In recent years, the use of AI in drug discovery and development has grown swiftly, marking a significant shift in how we understand, discover and develop new drugs. The technology promises to chip away at timelines and save the industry billions of dollars eventually. But those promises aren’t exactly new.  Even before ChatGPT became popular, many drug developers were working with high-end algorithms to “try to really hone in on databases,” said Andrew…

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