Minisymposium Presentation

Integrating computing-based solutions into translational biomedicine presents significant challenges, particularly in healthcare institutions underlying technical infrastructure and the adaptation of these solutions. Many facilities, especially in remote areas, lack infrastructure such as computing rooms and robust datacenters to support high-performance computing (HPC) systems required to handle the massive volumes of diverse generated biomedical data due to budget restraints preventing investment in cutting-edge technology. Secured AI tools must be tailored to complex clinical workflows, requiring resources for customization, testing, and regulatory compliance. Network reliability and cybersecurity issues complicate integration efforts for deploying AI-driven solutions. Data exchange across systems is hindered by fragmented sources, interoperability challenges, and varying data formats. The mix of structured and unstructured data, such as clinical records or genomic data, complicates integration. Data governance is a concern, especially regarding patient privacy, regulatory compliance like HIPAA, and ensuring ethical use of AI tools. For remote ICUs, limited bandwidth and the need for real-time decision-making add pressure, where delays can have critical consequences. Overcoming these physical, technical, and financial barriers, along with addressing the complexities in infrastructure, dataflow, and governance complexities, is crucial for successful integration of AI in healthcare and realizing its potential in improving patient care and outcomes.