Leveraging AI and Machine Learning in Embedded Technology for Healthcare Innovation

Purpose: This analysis examines how Artificial Intelligence (AI) [10] and Machine Learning (ML) [10] are transforming embedded healthcare technologies by enhancing diagnostics, predictive analytics, and personalized care. It also explores the challenges hindering their integration, including hardware limitations, regulatory complexities, and workforce training needs. Methodology: A review of recent advancements in AI and ML applications within healthcare highlights real-world examples, such as wearable devices and surgical robots, to illustrate their capabilities and limitations. Findings: AI and ML enable embedded healthcare technologies to process real-time medical data with unprecedented accuracy, enhancing patient outcomes by enabling wearable devices that continuously monitor vital signs and generate predictive alerts to prevent emergencies, and by powering surgical robots equipped with ML models that augment precision and decision-making to reduce human error and improve safety. Unique Contribution to Theory, Policy, and Practice: Theory enhances understanding of how embedded AI and ML systems impact healthcare outcomes, offering a foundation for future research into scalability and effectiveness. Policy emphasizes the need for robust regulatory frameworks to balance innovation with data security and equitable access. Practice provides actionable insights [12] for healthcare providers, including the importance of interdisciplinary collaboration and clinician training to responsibly leverage AI and ML tools.