Looking for Speakers

Call for Papers

Do you want to contribute?

Submit your Papers

If you have demonstrable experience with AI for RWE or RWD, we invite you to contribute your expertise and insights to our conference program.

Whether you want to contribute with a talk, case study or workshop,
submit your papers until May 17th.

Don't miss out on this unique platform to showcase your achievements, expand your network, and contribute to the further development of RWE and RWD.
We accept proposals covering one of the following topics:


AI-powered Data Analysis

How can AI help making sense of what you found? What can it do today, and what will it be able to do tomorrow.
We are looking for concrete cases, new applications and actionable insights.

How to use AI in observational research

Strong use cases, examples, new technologies or innovative procedures that have the potential to change the current dynamics and limitations of RWE/RWD research.
Ways of introducing AI into organizations and teams are also welcome topics for talks and workshops.

Multiscale and Multidimensional Data

Explore strategies for modelling complex domains within realistic constraints, focusing on principles and methodologies for defining fit-for-purpose models.

RWD Outliers and Anomalies

Unpacking the significance of exceptions in evidence and use cases that drive impactful insights powered or supported by Artificial Intelligence.

How to encourage learning and foster collaboration in the industry

In this dynamic field, numerous advancements have been made, yet many have encountered setbacks. Where can we consolidate the insights gained from these experiences? How might we foster collaboration effectively?

RWE – Contextualization of RWD

Diving into the nuances of context-dependent data interpretation, uncovering the challenges and methods for effective implementation and how AI can and will play a part.

Quality Data at the source and how this impacts the potential use of RWE generation using AI techniques

High quality RWD means AI algorithms/models can be built using accurate and comprehensive data which enables reliable predictions. How can AI help to improve results and limit RWE generalisability? Use cases preferred.

Ethical and regulatory considerations of the use of AI in RWE

Considerations of data privacy, algorithm transparency, and compliance with regulatory requirements. AI laws (e.g., Artificial Intelligence Act). How does this impact the work that we do?


AI-related workshops that profice RWE/RWD professionals with clear insights in how to introduce, apply and get the most our of technology.