FDA's new guidance on AI in drug development centers the risk introduced by the technology

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FDA's new guidance on AI in drug development centers the risk introduced by the technology

The FDA has released its first draft guidance on the use of artificial intelligence in drug development, marking a significant milestone after nearly a decade of machine learning advancements. This guidance comes in response to a surge in drug submissions that incorporate AI, with over 500 submissions reported since 2016, particularly in oncology, neurology, and gastroenterology. The agency aims to provide regulatory clarity, which has been identified as a major barrier to the broader adoption of AI technologies in the pharmaceutical industry.

The new guidance follows a reflection paper from the European Medicines Agency, highlighting a growing interest in AI's role throughout the drug product lifecycle. Industry experts, like Xiaoyan Wang from IMO Health, emphasize that regulatory clarity is crucial for overcoming obstacles in AI adoption. This shift in regulatory focus reflects the evolving landscape of drug development, where AI is increasingly seen as a valuable tool for innovation.

• FDA's guidance aims to clarify AI's role in drug development.

• Over 500 drug submissions with AI components have been received since 2016.

Key AI Terms Mentioned in this Article

Artificial Intelligence

Artificial intelligence refers to the simulation of human intelligence in machines, which is increasingly applied in drug development.

Regulatory Clarity

Regulatory clarity is essential for the adoption of AI technologies in the pharmaceutical industry, as highlighted by industry experts.

Machine Learning

Machine learning is a subset of AI that enables systems to learn from data, significantly impacting drug development processes.

Companies Mentioned in this Article

FDA

The FDA is responsible for regulating drug development and has recently focused on the implications of AI in this field.

IMO Health

IMO Health provides clinical data solutions and emphasizes the importance of regulatory clarity for AI adoption in healthcare.

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