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Artificial Intelligence Applied to clinical trials: opportunities and challenges.

Scott Askin1,2, Denis Burkhalter1,2, Gilda Calado1,3

  • 1Massachusetts College of Pharmacy and Health Sciences (MCPHS), 179 Longwood Ave, 02115 Boston, MA USA.

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Summary

Artificial Intelligence (AI) offers opportunities to streamline clinical trials (CTs) by improving recruitment and efficiency. However, ethical considerations and a lack of regulatory guidance present challenges to AI adoption in drug development.

Keywords:
Artificial Intelligence (AI)ChallengesClinical trials (CT)ImplicationsMachine learning (ML)Opportunities

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Area of Science:

  • Drug Development
  • Clinical Research
  • Artificial Intelligence

Background:

  • Clinical Trials (CTs) are crucial for safe drug development.
  • Personalized medicine necessitates advanced data-driven approaches.
  • Tailored Artificial Intelligence (AI) solutions are vital for efficient clinical research.

Purpose of the Study:

  • Identify opportunities, challenges, and implications of AI in CTs.
  • Analyze the current landscape of AI in clinical research.
  • Provide insights for AI adoption in drug development.

Main Methods:

  • Conducted an extensive literature search on AI and Machine Learning (ML) in CTs.
  • Focused on publications from the past 5 years in the US and Europe.
  • Included documents from Regulatory Authorities.

Main Results:

  • AI applications are prominent in oncology, particularly for patient recruitment.
  • Opportunities include reduced sample sizes, improved enrollment, and faster, optimized adaptive CTs.
  • Challenges involve ethical concerns, data standards, and limited regulatory guidance.

Conclusions:

  • AI in CTs is an emerging and rapidly evolving field.
  • Increased regulatory guidance is expected to accelerate AI adoption.
  • AI has the potential to enhance success rates, reduce trial burden, and expedite research and approval.