Machine Learning Applications in Medical Device Software: A State-of-the-Art Review and Future Directions

Abstract

Machine learning (ML) applications in medical device software have garnered significant attention for their potential to revolutionize healthcare delivery by enabling advanced analytics, predictive modeling, and personalized treatment strategies. This paper presents a state-of-the-art review and future directions of ML applications in medical device software. It begins by providing an overview of ML techniques commonly used in healthcare settings, including supervised learning, unsupervised learning, and reinforcement learning. Subsequently, the paper examines the current landscape of ML applications in medical device software, encompassing areas such as diagnostic imaging, patient monitoring, and treatment optimization. It explores the challenges and limitations of existing ML approaches, including data quality issues, interpretability concerns, and regulatory compliance complexities. Furthermore, the paper identifies future directions and emerging trends in ML-driven medical device software, such as federated learning, explainable AI, and regulatory frameworks for AI-enabled devices. By addressing these challenges and embracing emerging trends, stakeholders in the healthcare industry can harness the transformative potential of ML to improve patient outcomes, enhance clinical decision-making, and drive innovation in medical device software development.