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The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.

Mary Jo Lamberti1, Maria I Florez2, Hana Do2

  • 1Tufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, USA. mary_jo.lamberti@tufts.edu.

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Summary

Artificial intelligence (AI) and machine learning (ML) adoption in clinical development is low, with only 10.7% fully implementing AI/ML. Most companies are in early stages of AI/ML use for clinical trials.

Keywords:
Adoption maturityArtificial intelligenceClinical developmentClinical trial designClinical trialsGenerative AIInnovation adoptionMachine learningProtocol complexityProtocol designQuality by designReal-world dataResearch and development

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Area of Science:

  • Pharmaceutical Sciences
  • Clinical Research
  • Biotechnology

Background:

  • Artificial intelligence (AI) and machine learning (ML) use in drug discovery is established.
  • Quantifying AI/ML adoption, investment, and efficiency in clinical development remains underexplored.
  • Systematic measurement of AI/ML impact in clinical development is needed.

Purpose of the Study:

  • To measure the levels of AI/ML adoption in clinical development.
  • To assess investments and efficiencies gained from AI/ML in clinical development.
  • To identify challenges and opportunities associated with AI/ML implementation.

Main Methods:

  • A global online survey was conducted by the Tufts Center for the Study of Drug Development.
  • 302 responses were gathered from pharmaceutical companies, CROs, and technology vendors.
  • The survey assessed AI/ML implementation across 36 clinical trial activities, including investment and time savings.

Main Results:

  • 36.9% of respondents were not using AI/ML in clinical development activities.
  • 30.3% were piloting AI/ML, and 22.1% were partially implementing it.
  • Only 10.7% of organizations had fully implemented AI/ML with repeatable processes.

Conclusions:

  • AI/ML adoption in clinical development is in its nascent stages.
  • Significant opportunities exist for increasing AI/ML implementation and realizing its benefits.
  • Further research is needed to understand and optimize AI/ML's role in clinical trials.