Background <p>Artificial intelligence (AI) is revolutionizing healthcare by integrating into medical devices to improve diagnostic accuracy, treatment efficiency, and patient outcomes. AI technologies such as machine learning, deep learning, and edge computing play a crucial role in real-time data processing and integration into medical devices.</p> Objective <p>This paper provides an overview of the current state of AI in medical devices, emphasizing its integration into healthcare, technological advancements, and regulatory frameworks. It explores the trade-off between speed and accuracy in AI systems, as well as ethical and practical challenges.</p> Methods <p>A comprehensive review of the AI technologies in medical devices is presented, including the categorization of AI applications, regulatory guidelines, and the role of edge computing and Internet of Things (IoT) integration. Case studies showcasing AI medical devices focusing on speed vs. accuracy trade-offs are analyzed, along with the integration of AI in diagnostic imaging, personalized medicine, and predictive analytics.</p> Results <p>AI technologies have significantly enhanced diagnostic accuracy, clinical decision-making, and patient monitoring. The trade-off between speed and accuracy is addressed through innovative solutions like edge computing and machine learning. AI has shown potential in various fields such as radiology, cardiac monitoring, and surgical guidance, with case studies demonstrating both fast and accurate AI applications.</p> Conclusion <p>The integration of AI into medical devices is poised to transform healthcare by improving clinical workflows, decision-making, and personalized care. However, ethical considerations and regulatory challenges related to data privacy, algorithmic fairness, and patient safety must be addressed to ensure sustainable development. Continued innovation, supported by rigorous regulatory frameworks, will be essential for the effective and ethical deployment of AI in medical devices.</p>