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メニュー 戻る Data-Science-RWE-and-Artificial-Intelligence

Data Science, RWE & Artificial Intelligence

Data Science, RWE and AI have shown us that faster, better drug development is within our reach - and yet it always seems to be slightly out of our grasp, or is it?

This track will specifically focus on the progress made in the application of RWE and AI for product development (e.g., medical devices, medicines), in addition to the related area of data science, and where there are still challenges/hurdles to be overcome.

  • We will take a look at RWE/AI use cases in product development.
  • We want to hear from health authorities as to their progress in advancing thinking around RWE and AI, and look at intersection/interplay between these two areas.
  • We invite use cases on the use of AI for improving RWD data sources/datasets.
  • We are interested in the use of AI and/or RWD for evidence generation, such as in clinical trial design, including for refining inclusion/exclusion criteria, for validation of outcomes, and for long-term follow up etc.
  • We also invite use cases from pragmatic trials.
  • Current EU policies and regulation on RWE and AI in medicines compared with AI regulations for medical devices.

We will also aim to discuss the important area of ethical considerations for AI and the patient perspective and look for successful examples of how organisations may have addressed the challenge of bridging between the explorative data science vs. the traditional development space for clinical operations, statistics and epidemiological sciences.

Who is This Track Designed For?

This track is designed for anyone in, passionate about, curious on or working with Data Science, RWD/E and Artificial Intelligence in healthcare and medicines development.

Topic Leader

Gracy Crane

Gracy Crane

Policy Lead

Roche, United Kingdom

Estelle Michael

Estelle Michael

RWE Policy & External Engagement Lead

UCB, Belgium

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