Research Motivation

In the era of unprecedented advancements in artificial intelligence (AI) and its intersection with the medical domain, it is important to acknowledge that despite the immense potential and promise of data-driven innovations, their integration into clinical practice remains disappointingly sluggish. This persistent gap between the vast capabilities of AI and the realization of their transformative potential in healthcare underscores the critical need to address the multifaceted challenges that inhibit their widespread adoption. Recent scholarly publications have comprehensively outlined the various obstacles impeding the seamless transition of data-driven innovations into clinical practice. These challenges span the realms of legal, ethical, technical, and organizational dimensions, and are further intensified by the distinctive intricacies of the clinical domain (Gehrmann et al., 2023). Of particular note are the heightened regulatory demands imposed upon the healthcare sector, most notably exemplified in countries with stringent data protection requirements like Germany. Navigating this regulatory landscape to create an environment promoting rapid innovation represents a challenging undertaking.

In response to these challenges, researchers are proactively exploring novel avenues within the realm of Medical Data Science (MDS) to circumvent these barriers. One prominent research field revolves around the application of cutting-edge technological paradigms, including but not limited to Split Learning, Federated Learning, and Swarm Learning (Eskofier et al., 2022). These innovative techniques hold the promise of mitigating the inhibitions posed by clinical barriers and have demonstrated their potential to revolutionize the integration of AI-driven solutions in clinical settings.

Nevertheless, even as technological solutions offer hope and avenues for progress, it is vital to recognize that certain barriers persist and demand attention. A clear example of this can be seen in the domain of Nephrology, where state-of-the-art predictive models exhibit remarkable performance metrics, often surpassing traditional methodologies. Despite their evident utility, these models often face a key legal hurdle—they lack the necessary CE certification to be widely recognized as medical devices, preventing their official use in clinical practice (Medical Device Coordination Group, 2021). Consequently, the successful training of these models on sensitive clinical data, while a noteworthy accomplishment, remains a largely academic exercise, failing to bridge the crucial gap in the data-driven innovation cycle.

In light of these pressing challenges and untapped potentials, our research endeavors to explore the intricate landscape of data-driven innovation within the clinical domain. We aim to unravel the underlying intricacies of the barriers impeding adoption and explore potentials toward their resolution or mitigation. By examining both the technological innovations that hold transformative potential and the regulatory and organizational impediments that hinder their realization, we aspire to contribute to the eventual closure of the data-driven innovation cycle in clinical practice. Our research seeks to illuminate a path forward that ensures the seamless integration of AI-driven solutions into the clinical realm, fostering improved patient care and outcomes.