Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly transforming biomedical and life science engineering by enabling data-driven solutions to complex biological and healthcare challenges. The rapid growth of biomedical data from imaging, sensors, omics platforms, and clinical systems has created a strong need for intelligent computational methods that can extract meaningful patterns, improve prediction, and support informed decision-making. In this context, AI and ML provide effective tools for integrating engineering analysis with biological and medical applications. This presentation investigates the role of AI and ML in advancing innovation across biomedical and life science engineering, with particular emphasis on predictive modelling, intelligent system design, and automated data interpretation. Key application areas include medical image analysis, biomedical signal processing, digital biomarkers, health monitoring technologies, and computational support for diagnosis and therapeutic development. These approaches have the potential to improve analytical accuracy, enhance efficiency, reduce uncertainty, and accelerate translational impact in healthcare and life science research. The presentation also highlights important considerations for responsible implementation, including explainable AI, model transparency, data governance, and continuous validation of predictive systems in real-world biomedical settings. These factors are essential for ensuring trust, reproducibility, and practical relevance in intelligent life science applications. Finally, AI and ML are presented as enabling technologies for next-generation biomedical and life science engineering. By combining computational intelligence with engineering design and biological insight, they create new opportunities for precise, efficient, and patient-centred innovation in research, diagnostics, and translational practice.