Clinical Trials and Machine Learning: Regulatory Approach Review.

Journal: Reviews on recent clinical trials
Published Date:

Abstract

Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has provided important contributes to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has been constantly growing and this is now affecting the National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed or that are generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient's safety, health protection and fostering immediate access to effective treatments.

Authors

  • Diego Alejandro Dri
    Clinical Trials Office, Italian Medicines Agency (AIFA), Rome,Italy.
  • Maurizio Massella
    Clinical Trials Office, Italian Medicines Agency (AIFA), Rome,Italy.
  • Donatella Gramaglia
    Clinical Trials Office, Italian Medicines Agency (AIFA), Rome,Italy.
  • Carlotta Marianecci
    Department of Chemistry and Technology of Drugs (DCTF), University of Rome "La Sapienza", Rome,Italy.
  • Sandra Petraglia
    Pre-Authorization Department, Italian Medicines Agency (AIFA), Rome,Italy.