UK launches initiative to test AI in drug development

英國啟動計畫以測試人工智慧在藥物研發中的應用

更新於: 2026年6月10日上午03:00

On June 9, 2026, the UK government launched a landmark initiative to transform drug development through the power of artificial intelligence.

2026年6月9日，英國政府啟動了一項具有里程碑意義的計畫，旨在透過人工智慧的力量來徹底改變藥物開發。

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This controlled space allows researchers to collaborate directly with regulators, ensuring that AI-driven tools are safe, accurate, and capable of predicting drug side effects before they reach patients.

這個受控的空間讓研究人員能夠與監管機構直接合作，確保人工智慧驅動的工具在進入患者體內前，是安全、準確且具備預測藥物副作用能力的。

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The initiative addresses the daunting 90% failure rate in traditional drug development.

這項計畫旨在解決傳統藥物開發中高達90%的失敗率。

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What is the primary purpose of the 'regulatory sandbox' established by the MHRA?

To allow researchers to test AI tools for accuracy and safety with regulators.

To increase the cost of medical research.

To manufacture new pharmaceuticals exclusively.

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To allow researchers to test AI tools for accuracy and safety with regulators.

What is a major goal of the UK's initiative regarding drug development failure rates?

To address the high failure rate, which is approximately 90%.

To increase failure rates to improve long-term testing.

To stop all drug development trials permanently.

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To address the high failure rate, which is approximately 90%.

How does the initiative aim to help the NHS?

By eliminating the need for clinical trials.

By reducing hospitalizations from adverse drug reactions and saving costs.

By replacing all doctors with AI systems.

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By reducing hospitalizations from adverse drug reactions and saving costs.

What is the ambition for the 'AI for Science' strategy by 2030?

To ban the use of AI in all scientific research.

To conduct trials that last over ten years.

To develop trial-ready drugs within 100 days.

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To develop trial-ready drugs within 100 days.

Besides efficiency, what is another ethical goal mentioned in the initiative?

To decrease reliance on animal testing during preclinical stages.

To focus exclusively on elderly patients.

To prioritize only the most expensive medications.

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To decrease reliance on animal testing during preclinical stages.

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