Research on the Technical Bottlenecks, Regulatory Frameworks of Global Medical AI Industry Implementation and the Supporting System of Biomedical Engineering Discipline

Abstract

The escalating integration of Artificial Intelligence (AI) into healthcare presents transformative potential, yet its widespread global implementation is significantly impeded by multifaceted technical bottlenecks. This research systematically identifies and categorizes these primary obstacles, including data bias, model interpretability, robustness, computational demands, and interoperability issues, which collectively hinder the transition from research to clinical practice. Concurrently, an in-depth analysis of existing global regulatory frameworks, such as those established by the FDA and EMA, reveals their evolving but often insufficient capacity to address the unique complexities of medical AI, particularly concerning validation, bias mitigation, and transparency. The study posits that the biomedical engineering discipline is uniquely positioned to bridge this gap by developing critical supporting systems. This includes advancing explainable AI (XAI) methodologies, establishing robust validation tools and interoperability standards, and fostering interdisciplinary collaboration to translate ethical principles into practical engineering solutions. By proposing a comprehensive framework for biomedical engineering's contribution and offering evidence-based recommendations for policymakers and regulatory bodies, this work aims to foster responsible and effective medical AI adoption, ultimately enhancing global healthcare delivery and patient outcomes.