AI-Driven Transfer Learning for Medical Imaging and Sensor-Based Healthcare

Abstract Artificial intelligence (AI) technology is rapidly evolving in healthcare by optimizing the analysis of medical images and sensor data. Transfer Learning (TL) is now playing a vital role in this evolving field of healthcare through its applications in artificial intelligence systems, ensuring high performance even in situations where there is limited, heterogeneous, and variable data sets collected in different environments. In this review, we discuss the basic principles and architectures of artificial intelligence-driven transfer learning and its applications in various artificial intelligence systems, including recent developments in various forms of deep learning techniques such as convolutional learning, recurrent learning, transformer learning, multimodal learning, and foundation learning, and how these techniques are benefiting from transfer learning in optimizing images and sensor data in healthcare. Despite its advantages, transfer learning also presents some challenges in this field of healthcare. In conclusion, transfer learning combined with recent advancements in images and sensor technology presents a promising approach in artificial intelligence systems in optimizing images and sensor data in healthcare.