Advancing Biomedical Engineering With Artificial Intelligence and Machine Learning: A Systematic Review

Abstract

The inclusion of artificial intelligence (AI) and machine learning (ML) in biomedical engineering opens new frontiers of innovation and better decision-making and allows the art to cross new thresholds in healthcare technologies. This review focuses on the primary contributions of AI and ML to the advancement of biomedical engineering, particularly in the areas of diagnostic tools, predictive analytics, and personalized medicine. This will further allow us to identify possible the state-of-the-art solutions by using new frameworks for applications including medical imaging, wearables, and biomanufacturing. Also, it reflects on how ethics in AI and ML for biomedical challenges address important issues such as bias, privacy, and accountability. It also underlines how different opportunities and challenges can be opened or addressed by the integration of AI-driven systems in biomedical workflows: engineering, clinicians, and data scientists have to cooperate. Emerging technologies, including but not limited to deep learning, natural language processing, and reinforcement learning, are discussed for their potential to alter biomedical research and clinical practice. The work concludes with a look at the future of biomedical engineering, where AI and ML have brought a domain of synergy into innovation, better patient outcomes, and impactful advancement. It thus also provides a prescription for the need for ethics in the adoption of AI, together with collaborative efforts toward maximum transformative technology.