Through its involvement in BD4QoL, CSS has gained valuable insights into how emerging digital technologies can impact the generation of evidence in medicine. The traditional gold standard for demonstrating the safety and efficacy of a clinical intervention, particularly when involving drug administration, has been the principles of Evidence-Based Medicine (EBM).

With the advent of digital technologies, especially the ones based on artificial intelligence (AI), a shift in the paradigm becomes evident. The conventional approach to evidence generation has traditionally prioritized testing well-defined and robust research hypotheses on the collected data. In contrast, AI-driven EBM places a greater emphasis on discovering new evidence and generating new hypoteses through the analysis of collected data. The value is inherent in the data that needs to be extracted. This prompts a consideration of how medical evidence will be generated in the future and the potential need for adjustments to methods and standards.

The practical experience gained during the project highlights a significant distinction between these two approaches. While traditional EBM thrives in well-defined and controlled experimental conditions, the domain of digital technologies is often characterized by uncontrollable external factors. This was evident in the project, where challenges arose from necessary software updates in consumer technology occurring during a multi-year research project and for the fact that applications designed for patients had to coexist, in their devices, with other applications meant for everyday use.

These challenges, while posing new obstacles, also present opportunities to establish innovative methods for reliably producing evidence in a constantly evolving field. The dynamic nature of this landscape calls for adaptive strategies and opens the doors to novel approaches in evidence generation.