Under CopyrightLandschaft, AssafAssafLandschaftHirt, GarvinGarvinHirtIhlenfeld, AndréAndréIhlenfeld2024-08-232024-08-232024-05https://publica.fraunhofer.de/handle/publica/474054https://doi.org/10.24406/publica-359410.24406/publica-3594Rapid advances in healthcare data science, particularly in the fields of machine learning (ML) and artificial intelligence (AI), are transforming our ability to analyze and utilize real-world patient data. These technologies are enabling deeper insights into physiological systems and disease progression, while unlocking new possibilities for predictive modeling and personalized medicine. However, one of the major challenges remains the availability of AI-ready, real-world patient data repositories that are searchable, standardized, and interoperable. In this review, we identify existing healthcare data repositories and demonstrate how AI and ML techniques can harness these data sets to address critical gaps in medical research. As a test case, we explore the use of medical cannabinoids, where gaps in clinical trial data have hindered the full understanding of their therapeutic efficacy. By focusing on the establishment of a well-curated, real-world patient data repository, we illustrate how such resources are essential for advancing medical cannabinoids research, driving broader healthcare innovation, and enhancing clinical practice.enreal-world patient datamedical cannabis researchmedical data repositoriesKIAIPersonalized HealthcareArtificial IntelligenceReal-world evidence (RWE)Health informaticsposter